Barre Chord Toolbox (Matts Guitar Chords Book 2)

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For instance, signals which are entirely periodic perfect sine wave or random white noise are uninteresting musically — signals which fall between these two instances are where music is found. Finally, repetition is a key element of music, with melodic, chordal and structural motifs appearing several times in a given piece. To counteract this, in this thesis we will investigate how an integrated model of chords, keys, and basslines attempts to unravel the complexity of musical harmony.

The process of automatic chord extraction is shown in Figure 1. Labelling of frames is conducted by either the expert knowledge of the algorithm designers, or is extracted from training data for previously labelled songs. The final output is a file with start times, end times and chord labels. Audio Frames 0. Features are extracted directly from audio that has been dissected into short time instances known as frames, and then labelled with the aid of training data or expert knowledge to yield a prediction file. We detail these goals below. Automatic Transcription for Amateur Musicians Chords and chord sequences are mid-level features of music that are typically used by hobby musicians and professionals as robust representations of a piece for playing by oneself or in a group.

However, annotating the time-stamped chords to a song is a time-consuming task, even for professionals, and typically requires two or more annotators to resolve disagreements, as well as an annotation time of 3—5 times the length of the audio, per annotator [13]. In addition to this, many amateur musicians, despite being competent players, lack sufficient musical training to annotate chord sequences accurately. However, such websites are of limited use for Music Information Retrieval MIR by themselves because they lack onset times, which means they cannot be used in higher-level tasks see below.

Chords in Higher-level tasks In addition to use by professional and amateur musicians, chords and chord sequences have been used by the Music Information Retrieval MIR research community in the simultaneous estimate of beats [89] and musical keys [16], as well as in higher-level tasks 1 In this thesis, we describe low-level features as those extracted directly from the audio duration, zero-crossing rate etc. Tasks are defined as mid-level for instance if they attempt to identify mid-level features. Thus, advancement in automatic chord recognition will impact beyond the task itself and lead to developments in some of the areas listed above.

In any recognition task where the total number of examples is sufficiently small, an expert system will be able to perform well, as there will likely be less variance in the data, and one may specify parameters which fit the data well. At the other extreme, in cases of large and varied test data, it is impossible to specify the parameters necessary to attain good performance - a problem known as the acquisition bottleneck [31].

However, if sufficient training data are available for a task, machine learning systems may lead to higher generalisation potential than expert systems. This point is specifi- cally important in the domain of chord estimation, since a large number of new ground truths have been made available in recent months, which means that the generalisation of a machine-learning system may be tested. The prospect of good generalisation of an ML system to unseen data is the third motivating factor for this work. However, we must first investigate the literature to define the state of the art and see which techniques have been used by previous researchers in the field.

Once this has been conducted, we may address the second objective: developing a system that performs at the state of the art discussions of evaluation strategies are postponed until Section 2. This will involve the construction of two main facets: the development of a new chromagram feature vector for representing harmony, and the decoding of these features into chord sequences via a new graphical model. Finally, we will investigate and exploit one of the main advantages of deploying a machine learning based chord recognition: it may be retrained on new data as it arises.

Thus, our final objective will be to evaluate how our proposed system performs when trained on recently available training data and also test the generalisation of our model to new datasets. A graphical representation of our main algorithm, highlighting the thesis structure, is shown in Figure 1. We also provide brief summaries of the remaining chapters: Chapter 2: Background In this chapter, the relevant background information to the field is given.

We begin with some preliminary definitions and discussions of the function of chords in Western Popular music. We then give a detailed account of the literature to date, with partic- ular focus on feature extraction, modelling strategies, training schemes and evaluation techniques. Chapter 3: Chromagram Extraction Feature extraction is the focus of this chapter. We outline the motivation for loudness- based chromagrams, and then describe each stage of their calculation.

We follow this by conducting experiments to highlight the efficacy of these features on a trusted set of popular recordings for which the ground truth sequences are known. We begin by formalising the mathematics of the model and decoding process, before incrementally increasing the model complexity from a simple Hidden Markov Model HMM to HPA, by adding hidden nodes and transitions.

We finish this chapter by introducing a wider set of chord alphabets and discuss how one might deal with evaluating ACE systems on such alphabets. Chapter 5: Exploiting Additional Data In previous chapters, we used a trusted set of ground truth chord annotations which have been used numerous times in the annual MIREX evaluations. However, recently a number of new annotations have been made public, offering a chance to retrain HPA on a set of new labels. To this end, chapter 5 deals with training and testing on these datasets to ascertain whether learning can be transferred between datasets, and also investigates learning rates for HPA.

We then move on to discuss how partially labelled data may be used in either testing or training a machine learning based chord estimation algorithm, where we introduce a new method for aligning chord sequences to audio called jump alignment and additionally an evaluation scheme for estimating the alignment quality. Chapter 6: Conclusion This final chapter summarises the main findings of the thesis and suggests areas where future research might be advisable.

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Although the author has had publications outside the domain of automatic chord estimation, the papers presented here are entirely in this domain and relevant to this thesis. Ni, M. McVicar, R. De Bie. An end-to-end machine learning system for harmonic analysis of music. IEEE Transactions on Audio, Speech and Language Processing [81] [81] is based on early work not otherwise published by the author on using key- information in chord recognition, which has guided the design of the structure the DBN put forward in this paper.

The structure of the DBN is also inspired by musicological insights contributed by the thesis author. Early research by the author not otherwise published on the use of the constant-Q transform for designing chroma features has contributed to the design of the LBC feature introduced in this paper.

All aspects of the research were discussed in regular meetings involving all authors. The paper was written predominantly by the first author, but all authors contributed original material. McVicar, Y. Ni, R. It first led to a workshop paper [72], and [74] is an extension of this paper which includes also the Jump Alignment algorithm which was developed by Yizhao Ni but discussed by all authors.

The paper was written collaboratively by all authors. The second author of [73] contributed insight and exper- iments which did not make it into the final version of the paper, with remainder being composed and conducted by the first author. The paper was predominantly written by the first author. Conference Papers 1. Santos-Rodriguez and T. Leveraging noisy online databases for use in chord recognition. We also defined our main research objective: the development of a chord recognition system based entirely on machine-learning techniques, which may take full advantage of the newly released data sources that have become available.

We went on to list the main contributions to the field contained within this thesis, and how these appear within the structure of the work. These contributions were also highlighted in the main publications by the author. We begin by describing chords and their function in musical theory in section 2. A chronological account of the literature is given in section 2. Since their use is so ubiquitous in the field, we devote section 2. We conclude the chapter in section 2.

The definition and function of chords in musical theory is discussed, with particular focus on Western Popular music, the genre on which our work will be conducted. Although frequencies higher harmonics and lower subharmonics than f0 are produced simultaneously, we postpone the discussion of this until section 2. The word pitch, although colloquially similar to frequency, means something quite different. Pitch is defined as the perceptual ordering of sounds on a frequency scale [47]. Thus, pitch relates to how we are able to differentiate between lower and higher fundamental frequencies.

The distance interval between two adjacent pitches is known as a semitone, a tone being twice this distance. Notice from Equation 2. It has been noted that the human auditory system is able to distinguish pitch classes, which refers to the value of n mod 12 in Equation 2. This means that, for example, we hear two frequencies an octave apart as the same note.

This phenomenon is known as octave equivalence and has been exploited by researchers in the design of chromagram features see section 2. Pitches are often described using modern musical notation to avoid the use of irra- tional frequency numbers. In this discussion and throughout this thesis we will assume equivalence between sharps and flats, i. We now turn our attention to collections of pitches played together, which is intuitively the notion of a chord. The word chord has many potential characterisations and there is no universally agreed upon definition. Everyone agrees that chord is used for a group of musical tones, whilst Krolyi [42] is more specific, stating: Definition 2.

Two or more notes sounding simultaneously are known as a chord. Note here the concept of pitches being played simultaneously. Note also that it is not specified that the notes come from one particular voice, so that a chord may be played by a collection of instruments. Such music is known as Polyphonic conversely Monophonic. The Harvard Dictionary of music [93] defines a chord more strictly as a collection of three or more notes: Definition 3. Three or more pitches sounded simultaneously or functioning as if sounded simultaneously. Here the definition stretches to allow notes played in succession to be a chord - a concept known as an arpeggio.

In this thesis, we define a chord to be a collection of 3 or more notes played simultaneously. Note however that there will be times when we will need to be more flexible when dealing with, for instance, pre-made ground truth datasets such as those by Harte et al. In cases when datasets such as these contradict our definition we will map them to a suitable chord to our best knowledge. We now turn our attention to how chords function within the theory of musical harmony. In- stead, a key is used to define a suitable library of pitch classes and chords.

The most canonical example of a collection of pitch classes is the major scale, which, given a root starting note is defined as the set of intervals Tone-Tone-Semitone-Tone-Tone-Tone- Semitone. By far the most common chord types are triads, consisting of three notes. For instance, we may take a chord root a pitch class and add to it a third two notes up in the key and a fifth 4 notes to create a triad.

These chord types are known as major,minor and diminished triads respectively. We have presented the work here as chords being constructed from a key, although one may conversely consider a collection of chords as defining a key. This thorny issue was considered by Raphael [95] and a potential solution in modelling terms offered by some authors [16, 57] by estimating the chords and keys simultaneously see subsection 2. Keys may also change throughout a piece, and thus the associated chords in a piece may change a process known as modulation.

This has been modelled by some authors, leading to an improvement in recognition accuracy of chords [65]. These are known as the root position, first inversion and second inversion of a C Major chord respectively. When constructing 12—dimensional chromagram vectors see section 2. These issues will be dealt with in sections 2. A collection of chords played in sequence is known as a chord progression, a typical example of which is shown in Figure 2.

Certain chord transitions are more common than others, a fact that has been ex- ploited by authors of expert systems in order to produce more musically meaningful chord predictions [4, 65]. This concludes our discussion of the musical theory of chords. We now turn our attention to a thorough review of the literature of automatic chord estimation. The following sections deal in detail with the key advancements made by researchers in the domain. Mathematical Representation of Joint Time-chroma Mathematical foundation of Distributions [] chromagrams feature vectors Bello, J.

Hidden Markov Models [99] Gaussian emission probabilities, training from labelled data Yoshioka, T. Musical Key Extraction from Audio [90] Removal of background spectrum and processing of harmonics 2. Downbeats from an Audio File [88] estimation Varewyck, M. Estimation in Musical Audio [19] system Oudre, L. Cho, T. Using Recurrence Plots [20] smoothing Yoshii, K. Non-parametric Bayesian Chord Progression Analysis [] 2. We give a detailed account of the signal processing techniques associated with this feature vector in this section.

The word chroma is used to describe pitch class, whereas tone height refers to the octave information. Early methods of chord prediction were based on polyphonic note transcription [1, 17, 43, 61], although it was Fujishima [33] who first considered automatic chord recognition as a task unto itself. His Pitch Class Profile PCP feature involved taking a Discrete Fourier Transform of a segment of the input audio, and from this calculating the power evolution over a set of frequency bands.

Frequencies which were close to each pitch class C, C ] ,. For a given input signal, the PCP at each time instance was then compared to a series of chord templates using either nearest neighbour or weighted sum distance. Audio input was monophonic piano music and an adventurous 27 chord types were used as an alphabet. Salience of pitch class p at time t is estimated by the intensity of p, t th entry of the chromagram, with lighter colours in this plot indicating higher energy see colour bar between chromagram and annotation.

The reference ground truth chord annotation is also shown above for comparison, where we have reduced the chords to major and minor classes for simplicity. An alternative solution to the pitch tracking problem was proposed by Bello et al. An investigation into polyphonic transcription was attempted by Su, B. This means that one must make a trade-off between the frequency and time resolution. In practice this means that with short windows, one risks being unable to detect frequencies with long wavelength, whilst with a long window, a poor time resolution is obtained.

A solution to this is to use a frequency-dependent window length, an idea first implemented for music in [10]. In terms of the chord recognition task, it was used in [79], and has become very popular in recent years [4, 68, ]. The mathematical details of the constant-Q transform will be discussed in later sections. Some authors [90] have defined this as the background spectrum, and attempted to remove it in order to enhance the clarity of their features. One such background spectrum could be considered the percussive elements of the music, when working in harmony-related tasks.

An attempt to remove this spectrum was introduced in [84] and used to increase chord recognition performance in [96]. It is assumed that the percussive elements of a spectrum drums etc. The spectrum is assumed to be a simple sum of percussive and harmonic material and can be diffused into two constituent spectra, from which the harmony can be used for chordal analysis. The latter study also showed that employing post-processing techniques on the chroma including Fourier transform of chroma vector and increasing the number of states in the HMM by up to 3 offered improvements in recognition rates.

Harmonics It is known that musical instruments emit not only pure tones f0 , but a series of har- monics at higher frequencies, and subharmonics at lower frequencies. Such harmonics can easily confuse feature extraction techniques, and some authors have attempted to remove them in the feature extraction process [54, 65, 87, 90]. An illustrative example of sub harmonics is shown in Figure 2. They note that their new fea- tures matched chord profiles better than unprocessed chromagrams, a technique which was also employed by [65]. An alternative to processing the spectrum is to introduce harmonics into the modelling strategy, a concept we will discuss in section 2.

To counteract this, they constructed finer-grained chromagram feature vectors of 24, instead of 12, dimensions, allowing for flexibility in the tuning of the piece. Harte [38] introduced a tuning algorithm which computed a chromagram feature matrix over a finer granularity of 3 frequency bands per semitone, and searched for the sub-band which contained the most energy.

This was chosen as the tuning of the piece and the actual saliences inferred by interpolation. As an initial solution, some smoothing of the PCP vectors was introduced. This heuristic was repeated by other authors using template- based chord recognition systems see section 2. In [4], the concept of exploiting the fact that chords are relatively stable between beats [35] was used to create beat-synchronous chromagrams, where the time resolution is reduced to that of the main pulse.

Examples of smoothing techniques are shown in Figure 2. Popular methods of smoothing chroma features are to take the mean [4] or median [65] salience of each of the pitch classes between beats. Papadopoulus and Peeters [88] noted that a simultaneous estimate of beats led to an improvement in chords and vice-versa, supporting an argument that an integrated model of harmony and rhythm may offer improved performance in both tasks. A comparative study of post-processing techniques was conducted in [21], where they also compared different pre-filtering and modelling techniques.

This feature vector has also been explored by different authors also for key recognition [55, 56]. Within this work they estimated bass pitches from audio and add a bass probability into an existing hypothesis-search-based method [] and discovered an increase in recognition rate of, on average, 7. Such a bass chromagram has the advantage of being able to identify inversions of chords, which we will discuss in chapter 4.

A typical bass chromagram is shown, along with the corresponding treble chromagram, in Figure 2. This is known as a non-negative least squares problem [53] and can be solved uniquely in the case when E has full rank and more rows than columns. Within [64] NNLS chroma are shown to achieve an improvement of 6 percentage points over the then state of the art system by the same authors.

We have seen many techniques for chromagram computation in this Section. Some of these constant-Q, tuning, beat-synchronisation, bass chromagrams will be used in the design of our features see Chapter 3, whilst others Tonal centroid vectors will not. The author decided against using tonal centroid vectors as they are low-dimensional and therefore suited to situations with less training data, and also less easily interpreted than a chromagram representation.

We begin with a discussion of simple pattern-matching techniques. Typically, a 12—dimensional chromagram is compared to a binary vector containing ones where a trial chord has notes present. For example, the template for a C:major chord would be [1 0 0 0 1 0 0 1 0 0 0 0]. Each frame of the chromagram is compared to a set of templates, and the template with minimal distance to the chroma is output as the label for this frame see Figure 2. This technique was first proposed by Fujishima, where he used either the nearest neighbour template or a weighted sum [33] as a distance metric between templates and chroma frames.

Similarly, this technique was used by Cabral and collaborators [15] who compared it to the Extractor Discovery System EDS software to classify chords in Bossa Nova songs. An alternative approach to template matching was proposed in [],where they used a self-organising map, trained using expert knowledge. Further examples of template-based chord recognition systems can be found in [85]. This can be combated by either using smoothing methods as seen in 2. One of the most common ways of incorporating smoothness in the model is to use a Hidden Markov Model HMM, defined formally in Section 2.

An HMM models a time-varying process where one witnesses a sequence of observed variables coming from a corresponding sequence of hidden nodes, and can be used to formalize a probability distribution jointly for the chromagram feature vectors and the chord annotations of a song. In this model, the chords are modelled as a first-order Markovian process. Furthermore, given a chord, the feature vector in the corresponding time window is assumed to be independent of all other variables in the model. The chords are commonly referred to as the hidden variables and the chromagram feature vectors as the observed variables, as the chords are typically unknown and to be inferred from the given chromagram feature vectors in the chord recognition task.

See Figure 2. Arrows in Figure 2. Horizontal arrows represent the probability of one chord following another the transition probabilities , vertical arrows the probability of a chord emitting a particular chromagram the emission probabilities. Learning these probabilities may either be done using expert knowledge or using labelled training data. Hid- den states chords are shown as circular nodes, which emit observable states rectangular nodes, chroma frames.

In terms of chord recog- nition, the first example can be seen in the work by Sheh and Ellis [99], where HMMs and the Expectation-Maximisation algorithm [77] are used to train a model for chord boundary prediction and labelling. Although initial results were quite poor maximum recognition rate of The idea of real-time analysis was also explored in [], where they employ a simpler, template-based approach. An example of this can be seen in Figure 2. The two-chain HMM clearly has many more conditional probabilities than the simpler HMM, owing to the inclusion of a key chain.

As such, most authors disregard the diagonal transitions in Figure 2. This complex model has hidden nodes representing metric position, musical key, chord, and bass note, as well as observed treble and bass chromagrams. Dependencies between chords and treble chromagrams are as in a standard HMM, but with additional emissions from bass nodes to lower-range chromagrams, and interplay between metric position, keys and chords. This model was shown to be extremely effective in the audio chord estimation task in the MIREX evaluation, setting the cutting-edge performance of Hidden nodes Mi , Ki , Ci , Bi represent metric po- sition, key, chord and bass annotations, whilst observed nodes Cit and Cib represent treble and bass chromagrams.

However, for a given song example the observation is always fixed, so it may be more sensible to model the conditional P Y X , relaxing the necessity for the components of the observations to be conditionally independent. In this way, discriminative models attempt to achieve accurate input chromagram to output chord sequence mappings.

An additional potential benefit to this modelling strategy is that one may address the balance between, for example, the hidden and observation probabilities, or take into account more than one frame or indeed an entire chromagram in labelling a particular frame. This last approach was explored in [], where the recently developed SVM- struct algorithm was used as opposed to CRF, in addition to incorporating information about future chromagram frames to show an improvement over a standard HMM. Also, they note that their method can be used to identify genre in a probabilistic way, by simply testing all genre-specific models and choosing the model with largest likelihood.

A common method for doing this is to use a 12—dimensional Gaussian distribution, i. This technique has been very widely used in the literature see, for example [4, 40, 45, 99]. A slightly more sophisticated emission model is to consider a mixture of Gaussians, instead of one per chord. This has been explored in, for example, [20, 96, ]. A different emission model was proposed in [11], that of a Dirichlet model. Thus, a Dirichlet distribution is a distribution over numbers which sum to one, and a good candidate for a chromagram feature vector.

This emission model was implemented in the chord recognition task in [12], with encouraging results. Various ways of training such models are discussed in this section, beginning with expert knowledge. For example, they set high weights to primary chords in each key tonic, dominant and subdominant , additionally specifying that if the first three beats of a bar are a single chord, the last beat must also be this chord, and that chords non-diatonic to the current key are not permissible.

Using this key, chord estimation accuracy increased by an absolute Expert tuning of key-chord dependencies was also explored in [16], following the theory set out in Lerdahl [58]. A study of expert knowledge versus training was con- ducted in [87], where they compared expert setting of Gaussian emissions and transition probabilities, and found that expert tuning with representation of harmonics performed the best. However, they only used songs in the evaluation, and it is possible that with the additional data now available, a trained approach may be superior.

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Mauch and Dixon [63] also define chord transitions by hand, in the previously men- tioned work by defining an expert transition probability matrix which has a preference for chords to remain stable. Within, chord labels were annotated by hand and manually aligned to the audio, for use in a chord recognition task. This was ex- panded in work by Harte et al. A small set of 35 popular music songs was studied by Veronika Zenz and Andreas Rauber [], where they incorporated beat and key information into a heuristic method for determining chord labels and boundaries.

More recently, the Structural Analysis of Large Amounts of Music Information SALAMI project [13, ] announced a large amount of partially-labelled chord sequences and structural segmentations, amongst other meta data. We define sets above as Ground Truth datasets collections of time-aligned chord sequences curated by an expert in a format similar to Figure 2. Given a set of such songs, one may attempt to learn model parameters and probability distributions from these data.

Similarly for hidden features, one may count transitions between chords and learn common chord transitions as well as typical chord durations. This method has become extremely popular in recent years as the number of training examples has increased see, for example [20, 40, ? Such annotations are noisy and potentially difficult to use, but offer much in terms of volume of data available and are very widely used by musicians. For example, it was found in [60] that the most popular tab websites have over 2. A large number of examples of each song are available on such sites, which we refer to as redundancies of tabs.

For example, the authors of [60] found 24, redundancies for songs by The Beatles, or an average of The possibility of using such data to train a chord recognition model will be investigated in chapter 5. We discuss strategies for this in the current section. The former treats each song equally independent of song length, whilst the latter gives more weight to longer songs. Suppose also that the nth ground truth and prediction each have ni frames.

Clearly, there are many permissible chords available in music, and we cannot hope to correctly classify them all. Considering chords which do not exceed 1 octave, there are 12 pitch classes which may or may not be present, leaving us with possible chords. Such a chord alphabet is clearly prohibitive for modelling owing to the computational complexity and also poses issues in terms of evaluation. A step forward to a more workable alphabet came in , where Sheh and Ellis [99] considered 7 chord types maj,min,maj7,min7,dom7,aug,dim , although other authors have explored using just the 4 main triads maj,min,aug and dim [12, ].

A large chord alphabet of 10 chord types including inversions were recognised by Mauch [65]. One notable effect is that musical content can be quite different between albums, for a given artist. This is known as the Album Effect and is a known issue in artist identification [46, ]. In this case it is shown that identification of artists is more challenging when the test set consists of songs from an album not in the training set. For ACE, the problem is less well-studied, although, although intuitively the same property should hold.

However, informal experiments by the author revealed that training on a fixed percentage of each album and testing on the remainder resulted in lower test set performance. Despite this, the MIREX evaluations are conducted in this manner, which we emulate to make results comparable. Authors submit algorithms which are tested on a known dataset of au- dio and ground truths and results compared. We present a summary of the algorithms submitted in Tables 2.

Performance Year Category Sub. Uchiyama et al. Chroma, HMM 0. Ellis Chroma, HMM 0. Bello, J. Pickens Chroma, HMM 0. Ryynnen, A. Papadopoulos, G. Peeters Chroma, HMM 0. Khadkevich, M. Omologo Chroma, HMM 0. Lee - 0. Jhang, C. Lash Chroma, HMM 0. Weller et al. Chroma, SVMstruct 0. Mauch et al.

Oudre et al. Chroma, Template 0. Reed et al. Pauwels et al. Chroma, Key-HMM 0. Mauch and S. Cho et al. Ellis and A. Weller Chroma, SVMstruct 0. Ueda et al. Ni et al. Memorization of Ground Truth 0. Cho, J. Bello Chroma, HMM 0. Chroma, Language Model 0. Rocher et al. Bello and Pickens achieved 0. Interestingly, Uchiyama et. This year, the top performing algorithm in terms of both evaluations was Weller et al. Given this knowledge, the optimal strategy is to simply find a map between the audio of the signal to the ground truth dataset.

This can be obtained by, for example, audio fingerprinting [], although we took a simpler approach of making a simple chord estimate and choosing the ground truth which most closely matched this estimate. Suppose we have a collection of N songs and have calculated a chromagram X for each of them. Furthermore, given a chord, the dimensional chromagram feature vector in the corresponding time window is assumed to be independent of all other variables in the model.

The chords are commonly referred to as the hidden variables and the chro- magram feature vectors as the observed variables, as the chords are typically unknown and to be inferred from the given chromagram feature vectors in the chord recognition task. We now turn attention to learning the parameters of this model. Furthermore, we see strong self-transitions in line with our expectation that chords are constant over several beats.

Mean vectors bear close resemblance to the pitches present within each chord and the covariance matrix is almost diagonal, meaning there is little covariance between notes in chords.

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We saw that there is no well- defined notion of a musical chord, but that it is generally agreed to be a collection of simultaneous notes or arpeggio. We also saw how chords can be used to define the key of a piece, or vice-versa. Incorporating these two musical facets has been fruitful in the task of automatic chord recognition. Upon investigating the annual benchmarking system MIREX, we found that that the dominant architectures are chromagram features with HMM de- coding, although more complex features and modelling strategies have also been em- ployed.

We also saw that, since the testing data are known to participants, the optimal strategy is to overfit the test data as much as possible, meaning that these results may be misleading as a definition of the state of the art. By far the most prevalent features used in ACE are known as chromagrams see chapter 2. Our features are strongly related to these but are rooted in a sound theoretical foundation, based on the human perception of loudness of sound. This chapter is arranged as follows. Section 3. Sections 3. We conclude in section 3.

The human auditory system is complex, involving the inner, middle and outer ears, hair cells, and the brain. However, evidence exists that shows that humans are more sensitive to changes frequency magnitude, rather than temporal representations [24]. In previous studies, the salience of musical frequencies was represented by the power spectrum of the signal, i. However, there is no theoretical basis for using the power spectrum as opposed to the amplitude, for example, where we would use Xf,t.

This becomes an issue when summing over frequencies representing the same pitch class see section 3. Instead of using a loosely-defined notion of energy in this sense, we introduce the concept of loudness-based chromagrams in the following sections. The main feature extraction processes are shown in Figure 3.

Indeed, the empirical study in [29] showed that loudness is approximately linearly proportional to the so-called Sound Pressure Level SPL , proportional to log10 of the normalised power spectrum. A further complication is that human perception of loudness does not have a flat spectral sensitivity, as shown in the Equal-Loudness Contours in Figure 3. These Figure 3. Each line shows the current standards as defined in the ISO standard revision [39] at various loudness levels. These curves may be interpreted in the following way: each curve represents, at a given frequency, the SPL required to perceive loudness equal to a reference tone at 1, Hz.

As a solution to this variation in sensitivity, a number of weighting schemes have been suggested as industrial standard corrections. The most common of these is A- weighting [], which we adopt in our feature extraction process. The formulae for calculating the weights are given in subsection 3. This downsampling is used to reduce computation time in the feature extraction process.

The intuition behind this concept is that percussive sounds do not contribute to the tonal qualities of the piece, and in this sense can be considered noise. Notice that the the harmonic component contains a more stable horizontal component, whilst in the percussive component, more of the vertical com- ponents remain. Audio inspection of the resulting waveforms confirmed that the HPSS technique had in fact captured much of the harmonic component in one waveform, whilst removing the percussion. Discarding the percussive component of the audio, we now work solely with the harmonic component.

Deviating from this assumption could lead to note frequencies estimated incorrectly, meaning that the chromagram bins are incorrectly estimated which could degrade performance. Our tuning method follows that of [26], where an initial histogram is calculated of all frequencies found, relative to standard pitch. The centre frequencies of the spec- trum can then be augmented according to this information.

We provide an illustrative example of the tuning algorithm in Figure 3. The most natural choice of transform to the frequency domain may be the Fourier transform [9]. However, this transform has a fixed window size, meaning that if too small a window is used, some low frequencies may be missed as they will have a period larger than the window.

Conversely, if the window size used is too large, a poor time resolution will be obtained. A balance between time and frequency resolution can be found by having frequency- dependent window sizes, a concept that can be implemented via a Constant-Q spec- trum. Let F be the set of frequencies on the equal-tempered scale possibly tuned to a particular song, see subsection 3. Then Xf,t reflects the salience at frequency f and frame t. Instead, we centre the windows on every sample from the signal.

In addition to this, we found that by choosing larger windows than are specified by the constant-Q ratios, performance increased. Note that this is equivalent to using a larger value of Q and then decimating in frequency. We found that a power factor of 5 worked well for treble frequencies, whilst 3 was slightly better for bass frequencies, although results were not particularly sensitive to this parameter. As described in subsection 3. This is achieved by first computing the sound pressure level of the spectrum, and then correcting for the fact that the powers of low and high frequencies require higher sound pressure levels for the same perceived loudness as do mid-frequencies [29].

Given the constant-Q spectrogram representation X, we compute the Sound Pres- sure Level SPL representation by taking the logarithm of the energy spectrum. A reference pressure level pref is needed, but as we shall see in subsection 3. A small constant may be added to kXf,t k2 to avoid numerical problems in this calculation, although we did not experience this issue in any of our data. We are left with a sound pressure level matrix that relates to the human perception of the loudness of frequency powers in a musical piece.

Taking advantage of octave equivalence, we now sum over frequencies which belong to the same pitch class. It is known that loudnesses are additive if they are not close in frequency [97]. To this we add artificial beats at time 0, and the end of the song, and take the median chromagram vector between subsequent beats to beat-synchronise our chromagrams.

Note also that the A-weighting scheme used is a non-linear addition, such that its effect is not lost in the normalisation. We begin by explaining how we obtained ground truth labels to match our features. Subsequently, we comprehensively investigate all aspects of our chromagram feature vectors. This is easily obtained by sampling the ground truth chord annotations when available according to the beat times extracted from the procedure noted in subsection 3. When a chromagram frame falls entirely within one chord label, we assign this chord to the frame. When the chromagram frame overlaps two or more chords, we take the label to be the chord that occupies the majority of time within this window.

This process is shown in Figure 3. Chords are then mapped to a smaller chord alphabet such as those listed in sub- section 2. Wishing to see the effect that each stage of processing had on recognition accuracy, we incrementally increased the number of signal processing techniques. We refer to the loudness-based chromagram described in Sections 3.

All feature vectors were range-normalised after computation. We show the chroma- grams for a particular song for visual comparison in Figure 3. Performance in this song increased from Moving on, we see that the energy from the dominant pitch classes A and E are incorrectly mapped to the neighbouring pitch classes, which is corrected by tuning estimated tuning for this song was cents. Calculating the loudness of this chromagram enhances the loudness of the pitches A and E, which is further enhanced by A-weighting.

Finally, beat-synchronisation means that each frame now corresponds to a musically meaningful time scale. An HMM as per Section 2. Chord similarity per song was simply measured by number of correctly identified frames divided by total number of frames and we used either ARCO or TRCO see subsection 2. Overall performances are shown in Table 3. We also conducted the Wilcoxon rank sum test to test the significance of improvements seen. Table 3. Investigating each component separately, we see that Harmonic Percussive Sound Separation decreases the performance slightly over the full waveform.

This decrease is small in magnitude and can be explained by the suboptimal selection of the power factor in the chromagram extraction1. By far the largest improvement can be seen by taking the log of the spectrum LBC, row 4 , with a very slight improvement upon adding A-weighting. Although this increase is not significant, we include it in the feature extraction to ensure the loudness we calculate models the human perception of loudness.

The results presented here are comparable to the pretrained or expert systems in MIREX evaluations in section 2. Although applying HPSS to the spectrogram degraded perfor- mance slightly, the change is small in magnitude around We saw how the notion of perception of loudness was difficult to define, although under some relaxed assumptions we can model it closely. One of the key findings of these studies was that the human auditory response to the loudness of pitches was non-linear with respect to frequency.

With these studies in mind, we computed loudness based chromagrams that are rigorously defined and follow the industrial standard of A-weighting of frequencies. These techniques were enhanced by injecting some musical knowledge into the fea- ture extraction. For example, we tuned the frequencies to correspond to the musical scale, removed the percussive element of the audio, and beat-synchronised our features. Experimentally, we saw that by introducing these techniques we achieve a performance of Having described our feature extraction process in chapter 3, we must decide on how to assign a chord, key and bass label to each frame.

As shown in subsection 2. However, it is possible that these models overfit the available data by hand-tuning of parame- ters. We will counter this by employing machine learning techniques to infer parameter settings from fully-labelled data, and testing our results using cross-validation.

The remainder of this chapter is arranged as follows: section 4. In section 4. Moving on to section 4. We conclude this chapter in section 4. The hidden variables correspond to the key K, the chord label C and the bass B annotations. Under this representation, a chord is decomposed into two aspects: chord label and bass note. Accordingly, we compute two chromagrams for two frequency ranges: the treble chromagram Xc , which is emitted by the chord sequence c and the bass chromagram Xb , which is emitted by the bass sequence b.

The reason of applying this decomposition is that different chords can share the same bass note, resulting in similar chroma features in the low frequency domain. We hope that by using separated variables we can increase variation between chord states, so as to better recognise in particular complex chords. Note that this definition of bass note is non-standard: we are not referring to the note which the bass instrument i. HPA has a similar structure to the chord estimation model defined by Mauch [62]. Note however the lack of metric position we are aware of no data to train this node , and that that the conditional probabilities in the model are different.

Bass notes were extracted directly from the chord labels, whilst for keys we used the corresponding key set from the MIREX dataset2 although this data is not available to participants of the MIREX evaluations. The amount of key data in these files is sparse when compared to chords. To counteract this, following Ellis et. Model parameters were then transposed 12 times, leaving us with approximately 12 times as much training data for the hidden chain. Key to chord transitions were also learnt in this way. However, this simplification reduces computational and statis- tical cost and results in better performance in practice.

Given a chord, key and bass alphabet size of sizes Ac , Ak , Ac , respectively, the time complexity for Viterbi decoding a song with T frames is O Ac 2 Ak 2 Ac 2 T , which easily becomes prohibitive as the alphabets become of reasonable size. To coun- teract this, we employ a number of search space reduction techniques, detailed below. Chord Alphabet Constraint It is unlikely that any one song will use all the chords available in the alphabet in a song. Therefore, we can reduce the number of chord nodes to search if a chord alphabet is known, before decoding.

To achieve this, we ran a simple HMM with max-gamma decoder [92] over the observation probability matrix for a song using the full frequency 0 range , and obtained such an alphabet, Ac. Chord to Bass Constraint Similarly, we expect that a given chord will be unlikely to emit all possible bass notes. We will therefore employ these parameters throughout the remainder of this thesis. We will begin with a baseline HMM approach to chord recognition, which can be realised as using HPA with all key and bass nodes disabled.

To ensure that all frequencies were covered, we ran this model using a chromagram that covered the entire frequency range A1-G]6. Penultimately, we allowed the model to detect bass notes, and split the chromagram into a bass A1-G]3 and treble A4-G]6 range, before investigating the full HPA architecture.

Note that the bass and treble chromagrams are split arbitrarily into two three octave representations. Under this setting, each fully-labelled training song is designated to be either a training song on which to learn parameters, or a test song for evaluation. As previously mentioned, to investigate the effect that various hidden and observed nodes had on performance, we disabled several of the nodes, beginning at first with a simple HMM as per chapter 3. A Hidden Markov Model with hidden nodes representing chords and an emission chromagram ranging from A1 to G]6.

As above, with an additional hidden key chain and key to chord links. As above, with distinct chroma for the bass A1-G]3 and treble A4-G]6 frequencies, and an accompanying chord to bass node. Full Harmony Progression Analyser, i. As can be seen directly from Table 4. In general, we expect the training performance of the model to increase as the complexity of the model increases down the rows, although the HMM appears to buck this trend, offering superior performance to the Key-HMM rows 1 and 2.

However, this pattern is not repeated in the test scenario, suggesting that the HMM is overfitting the training data in these instances. The fact that performance increases as the model grows in intricacy demonstrates the power of the model, and also confirms that we have enough data to train it efficiently. This result is encouraging, as it shows that it is possible to learn chord models from fully-labelled data, and also gives us hope that we might build a flexible model capable of performing chord estimation different artists and genres.

The generalisation potential of HPA will be investigated in chapter 5. Over a given number of cross-validations in our case, , we wish to see if the improvements we have found are genuine enhancements or could be due to random fluctuations in the data. Upon inspecting the results in Table 4. Therefore, 1-sided, paired t-tests were conducted to assess if each stage of the al- gorithm was improving on the previous one.

With the sole exception of HMM vs.

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We measured key accuracy in a frame-wise manner, but noticed that the percentage of frames where the key was correctly identified was strongly non-Gaussian, as we were generally either predicting the correct key for all frames or the incorrect key. Providing a mean of such a result is misleading, so we chose instead to provide the histograms which show the average performance over the repetitions of 3-fold cross-validation, shown in Figure 4.

The performance here is not as high as we may expect, given the accuracy attained on chord estimation. Reasons for this may include that the key nodes see Figure 4. Investigating these scenarios is part of our future work. These are shown for the final two models in Table 4. Table 4. The recognition rate is also high in general, peaking at Paired t—tests were conducted as per subsection 4. What remains to be seen is how bass note recognition affects chord inversion accu- racy, although this has been noted by previous authors [65].

However, as mentioned in chapter 2, there are many other chord types available to us. We therefore defined 4 sets of chord alphabets for advanced testing, which are listed in Table 4. Quads is an extension of Triads, with some common 4-note 7th chords. We did not attempt to recognise any chords containing intervals above the octave, since in a chromagram representation we can not distinguish between, for example, C:add9 and Csus2.

Reading the ground truth chord annotations and simplifying into one of the alphabets in Table 4.


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Larger chord alphabets such as MM pose an interesting question for evaluation. For example, how should we score a frame whose true label is A:min7 but which we label as C:maj6? Both chords share the same pitch classes A,C,E,G but have different musical functions. For this reason, we now turn our attention to evaluation schemes.

However, for complex chords the question is more open to interpretation. The two chords share the same base triad and 7th, but the exact pitch classes differ slightly, as well as the order in which they appear in the chord. We describe here three different similarity functions for evaluating chord recognition accuracy that, given a predicted and ground truth chord frame, will output a score between these two chords 1 or 0. We begin with chord precision, which measures 1 only if the ground truth and predicted chord are identical at the specified alphabet.

Next, Note Precision scores 1 if the pitch classes in the two chords are the same and 0 otherwise. Finally, we investigate using the MIREX-style system, which scores 1 if the root and third are equal in predicted and true chord labels meaning that C:maj and C:maj7 are considered equal in this evaluation , which we denote by MIREX. In keeping with the MIREX tradition, we also increased the sample rate of ground truth and predictions to 1, Hz in the following evaluations to reduce the potential effect of the beat tracking algorithm on performance.

Note P. Secondly, we notice that performance of all types decreases as the chord alphabet increases in size from minmaj 25 classes to Quads classes , as expected. Performance drops most sharply when moving from Triads to MM, possibly owing to the inclusion of 7th chords and their potential confusion with their constituent triads. Comparing the different evaluation schemes, we see that Chord Precision is always lower than Note Precision as expected , and that the gap between an HMM and HPA increases as the chord alphabet increases 3.

Although we have already highlighted the weaknesses of the MIREX evaluations in the current section and in chapter 2, it is still clear that HPA performs at a similar level to the cutting edge. We formulated HPA mathematically as Viterbi decoding of a pair of bass and treble chromagrams in a similar way to an HMM, but on a larger state space consisting of hidden nodes for chord, bass and key sequences. We noted that this increase in state space has a drawback: computational time increases significantly, and we introduced machine-learning based techniques two-stage prediction, dynamic pruning to select a subspace of the parameter space to explore.

Bass note accuracy peaked at However, one of the main benefits of designing a machine-learning based system is that it may be retrained on new data as it arises. Recently, a number of new fully-labelled chord sequence annotations have been made available. These include the USpop set of tracks [7] and the Billboard dataset of 1, tracks, for which the ground truth has been released for the remainder being saved for test data in future MIREX evaluations [13].

We may also make use of seven Carole King annotations1 and a collection of five tracks by the rock group, Oasis, curated by ourselves [74]. Such UCSs have been shown by ourselves in the past to improve chord recognition when training data is limited [73]. To retain our focus we will structure the experiments in this chapter to investigate the following questions: 1. How similar are the datasets to each other? Can we learn from one of the datasets to test in another a process known as out of domain testing?

Are any sets similar enough to be combined into one unified training set? How fast does HPA learn? Can we use Untimed Chord Sequences as an additional source of information in a test setting? Can a large number of UCSs be used as an additional source of training data? We will answer the above questions in this chapter by following the following struc- ture. Section 5. The mathemat- ical framework for using chord databases as an additional data source is introduced in section 5. We then move on to see how these data may be used in training in section 5. Such training data may come from varying distributions, which may affect the type of model learnt, and also the generalisation of the model.

For instance, one can imagine that given a large database of classical recordings and corresponding chord sequences on which to train, a chord recognition system may struggle to annotate the chords to heavy metal music, owing to the different instru- mentation and chord transitions in this genre. In this section we will investigate how well an HMM and HPA are able to transfer their learning to the data we have at hand. Billboard This dataset contains tracks by artists which have at one time appeared on the US Billboard Hot chart listing, obtained with thanks from [13].

We removed songs which were cover versions identified by identical title as well as 21 songs which had potential tuning problems confirmed by the authors of [13] ; we were left with key and chord annotations. Worth noting, however, is that this dataset is not completely labelled. Specifically, it lacks exact onset times for chord boundaries, although segment onset times are included.

An example annotation is shown in Figure 5. To counteract this, we extracted chord labels directly from the text and aligned them to the corresponding chromagram many thanks to Ashley Burgoyne for running our feature extraction software on the music source , assuming that each bar has equal duration. This process was repeated for the key annotations to yield a set of annotations in the style of Harte et al.

USpop This dataset of tracks has only very recently been made available, and is sampled from the USpop dataset of 8, songs [7]. Full chord labels are available, although there is no data on key labels for these songs, meaning they unfortunately cannot be used to train HPA. Despite this, we may train an HMM on these data, or use them exclusively for testing purposes. These data are not currently complemented by key annotations. Therefore, we begin by deploying an HMM on all datasets. Results are shown in Table 5. Results for Chord Precession are also shown in Figure 5. Worth noting, however, is that these extreme values are seen when there are few training examples training set Carole King or Oasis.

This is due to the model lacking the necessary information to train the hidden or observed chain. It is extremely unlikely, for example, that the full range of Quads chords are seen in the Oasis dataset, meaning that these chords are rarely decoded by the Viterbi algorithm although small pseudocounts of 1 chord were used to try to counteract this.

These extreme cases highlight the dependence of machine-learning based systems on a large amount of good quality training data. When testing on the small datasets Carole King and Oasis , this becomes even more of an issue, in the most extreme case giving a training set performance of In cases where we have sufficient data however train sets Billboard, MIREX and USpop , we see more encouraging results worst performance at minmaj was Performance in TRCO generally de- creases as the alphabet size increases as expected, with the sharpest decrease occurring from the Triads alphabet to MM.

We now move on to see how HPA deals with the variance across datasets. Figure 5. The effect of overfitting on limited training data is most obviously seen in Figure 5. Indeed, the largest difference between train and test per- formances under the minmaj alphabet is at most It is also encouraging to see that by training on the Billboard data, we attain higher performance when testing on MIREX Results for these experiments are shown in Table 5.

There are tons of waves available on the web both for free or for purchase. It would be nice to be able to make use of them. When combined with an improved annotation tool, I could also see this as a way to incorporate more off-the-wall sounds into a score without having users ask for each individual case. If you want a song to start with birds chirping or a subway train roaring out of a tunnel then, as long as a sound file can be obtained, an instrument variant could be created using the sound file; maybe linked to a note on the virtual keyboard.

Endless possibilities. I want the help functions in Dutch, not in Englisch. Help function is supposed to lead you to the user manual. This one is change in fuction of the software selected language when you run it. Hope this helps. Also, little things like, the feature where you could hear the pitch while you were changing the tunings. Guitar Pro really lacks possibility to record audio over tracks. However good vocals notation may be, it is still required to write vocal guide with live voice.

At the moment I record vocal guides in external files. I find the editing window a bit frustrated. Very often I input the numbers in the TAB or standard stave, just as everybody does. The time values such as crotchet have to be changed many times during composing. However, the editing panel is far away on the left. That means I have to move the cursor frequently in a long distant. That is inconvenient, makes me tired and slow down composing speed.

What I want to say is the way of composing music in the editing window. As the music sheet is edited from the left to the right, the editing window can be improved in this way. What about if a simple tool box always display and follow the cursor while editing. The icons in the tool box can be set by user. For example, selecting time values in the tool box using the mouse and enter numbers in the stave by keyboard.

Much more is that when the user move the cursor from the left to right of the last note to be editing in the same bar or the immediate next bar, the cursor automatically select the line or space in the stave or any lines in the TAB. At the same time, the simple tool box always follow the cursor there which facilitates changing of time values.

This function can also apply to any note it points. Another improvement about editing is to add a icon bar containing different types of time values, and even some patterns of time values. This may be a good way to get people into the program for cheap and then later they can pay for an upgrade. The other thing that really needs to happen with this program is improve the look of the music. It seems, as far as I can tell, impossible to make professional looking transcriptions like you can with Sibelius, Finale or even Logic Pro. For example often there are ridiculous amounts of ties covering the page.

This is the only feature that will compel me to upgrade. The ability to print only selected tracks, rather than ALL the tracks at once. It took me 6 months to be able to read music. It took me only 2 weeks to be able to read and write drum notation on the stave using the numeric keypad. I have no idea what everyone here is talking about, but ussing the numeric keypad to enter the drum notes directly on the stave is the FASTEST way you could ever write drum notation. I think people are just too lazy to learn how to read and write drum notation and learn the corresponding numeric shortcuts.

Takes 2 weeks to memorize! Not too hard! I have some idea Click mouse to add note, import Image file. Export gif How to play the song, Export the fretboard in gif animated. Please fix the palm mute. Quite frankly, I will not be able to use a new version of guitar pro without the drum tab view as an option.

I understand that Arobas is trying to look more professional by disabling that option, but as someone who has used GP for YEARS, and has purchased the product, I am totally disappointed with the lack of respect for important features, and the regression thereof. But since I received the program and used it for the first time I have BARELY used it, do to the various regression in features, and the lack of compatibility with older.

Without significant features being resurrected, I am sad to say I will no longer be purchasing Arobas products, and will be stuck using GP5 forever. Since I have been using GP6 exclusely to learn certain stuff and to quickly note down ideas, it would be great if come back to your core audience and your core expertice: Provide people a convenient, fast and easy way to learn parts, songs and to compose.

I agree with Manuel. I would, however, like a very basic midi sound for each instrument for the tabs in the lite version though. There needs to be a feature that allows you to sync a music file to the tabs so that you can press play once and listen to the real song but still have the tabs scrolling with you as you play.

This is About Ubuntu 11 and 10 running guitar pro 1. Guitar pro 6 does not install with new Ubuntu version that is Give relevant info about dual boot options until your products actually works. Work on that. Use tuxguttar for batch conversion of other file formats, since your import function does not work.

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Fix install problems and import problems first, then give guitar pro 6 a free upgrade or money back. You are selling a product that is not functional, now you want more money? Fix the old product first. I would like to have an interface with the guitar neck like they did in the last rockband 3. It would be very useful if you add some place where we can store our riffs, and something like a workspace where we can drag n drop riffs to a tab and hear how good they sound together. Fantasitic tool, but, it often messes up the measures that come after by adding Rests and pushing the remaining notation out too far.

For example, a solo with a lot of empty measures after it measures all have one whole Rest will suddenly see these measures filled with an odd assortment of Rest sizes, eventually pushing out the next solo past where it is supposed to begin in the score. I want that because when you try to load files that has instrument that are not in the soundbanks it sound weird compare to the original song.

If thats not possible then make a editor so we could add our own instrument. I would like to see that you can customize the notes on the staff to be different size drums or cymbals. Also I liked that you could press 7 at high G, F and E to get the cowbell but I would like to have octobans, timbaltos and other percussive instruments and have the user place it wherever they want to on the staff and map it to any number on the keyboard and of course to be able to save the drum setup and be able to save multiple setups.

Implement Drag and drop to make score editing easier 2. I would like to see a longer Recently used list or a way to organise tabs in to some kind of play list. That way you would be able to say keep all the tabs that you are currently practising in different lists depending on the type of tab they are. Write name chords faster.

I want to be able to map drums again just like in GP5. I find myself using GP5 and GP6 simultaneously because I am needing to map drums in one and guitars in the other. Also, please include lower tunings such as Dropped B or even lower. I know you can manually adjust the tuning that low, but the midi sounds become out of tune when they are that low…. Also add so i can have different sounds on the same track like in guitar pro 5. Then you create a new project i think i should start out with a blank page instead of a steel guitar, it will also make guitar pro more universal.

Also then you change the sound of an created instrunment, then should the presets of the amp and effects change as well. And please add one more of your heavy solos in F, i love them. I agree with Tom Warnke. A bit or at least bit compatible version! Anomuumi: if you wish, I can forward a tutorial to run Guitar Pro on a 64 bit compatible version. Add or integrate a Simple to use auto accompaniment tool similar in style to ChordPulse or Easyband android which are sort of like Band in A box but much nicer simple interfaces. At the moment I use ChordPulse or Easyband to create a chord progression with a common rhythm backing track, then import via midi file then remove or alter the tracks in Guitar Pro to start building songs.

Simple way to swap an instrument associated with a track after laying down the track. For example I use a 5 string bass and if I want to reassign a 4 string bass track to a 5 string I have to copy the entire track into a new 5 string track. A simple way to copy or overlay the chord names from the bars of another instrument. This is useful when generating a bass line over the chords of a guitar part. Give user option for larger controls.

Provide larger buttons compatible with touch screens, perhaps with a tear away transport controls. This feature already exists in GP6. I like to build tracks in here for song writing and then demo them for other people by recording my guitar lines and vox over the track. It helps it be more realistic but it would be nice if there was some sort of humanistic function I could use that would maybe purposely offset the track within a certain percentage of the timing.

Anyways, not a huge thing but detail always counts. I really want to have Phrase marking: a curved line upon group of notes. The drums need a option for choking a cymbal. Also comping slashes would be nice for the guitar as well as some better sound banks for the brass section. It would really be nice as I am a college student going for a music major.

I dont expect super real or natural tone, all that I need is clear, straigt sound of each note which will help me clearly hear all my tracks. It would be nice to be able to open more files then now, just like tabs in any web-browser. And there should be an ability to restore previously opened files after crash or any other emergensy exit. Also, it might be good to have something like a GP View Tool or Lite Version, without all the editing functions, just to open one file and possibly play it. Keep up the good work! I have a couple suggestions, mostly for writing purposes.

A little tweaking with the current notation input system would be nice. Sibelius uses a number pad system to input durations which is very quick and easy to use. Something like that would be great. Finale has the same problem, having to move the cursor back over to the toolbars is a very inefficient way of writing.

Secondly, the ability to select which instruments are in view at one time would be incredibly helpful. In this program, I often find myself scrolling around a lot or having to look at a very cluttered scored when all I need is to see 2 or 3 instruments to most efficiently write a part. This is an extremely useful side of the program, and perhaps having rewire capability directly into DAW software as almost an instrument plug-in would be incredible and time saving. I love Guitar Pro but there is a major problem with 6 that needs to be fixed. The problem is whenever I export MIDI data from a song part such as a guitar none of the nuances such as bends and slides are exported with the MIDI data, and in most cases the timing is different from the tab that it was exported from.

I have Guitar Pro 5, 5. There needs to be a way to extract the file to an MP3 also could you add pick scrapes and feedback fdbk. Also have you considered a reader version with minimal MIDI sound bank for lower price? Another thing that should be brought back is the ability to make gradual tempo changes like in GP5. Hello John, thanks for your feedback. Be able to switch back to the old look of GP 5 and have drums written the same way. Pretty much I just wanna pay for the same look and function, just more stuff added to as far as instruments. I would like to see this unfortunate limitation removed.

I consider this to be a problem fix. There are general rules about note stem directions in music notation depending on the vertical position on the staff, and I think the program is trying to enforce the rules rigidly. I think we should be able to reverse stem direction anywhere we deem appropriate.

This is especially important for classical guitar for which the top voice is usually written with upward stems and a lower voice is written with downward stems no matter what vertical position the note has. I consider this to be an easy fix that could be in a maintenance update. It looks like something like this already exists for the TAB staff. This could make note entry much more efficient. Present v6 behavior could also be an option. I consider this to be a very desirable enhancement. A function that lets you record a guitar and the program trys to calculate from the pitch of the sound that how its played and greates a tabs that are like the song you played on the guitar.

Hello, thank you for your work: you are very easy to work on music. In Guitar pro 7, I would like you to do a wide choice of drum set. In Guitar pro 6 strongly this is not enough. Thank you in advance. I would like to be able to move up and down multiple notes at once by semitone, because it take to much time to move each note separately when you need lower or rise some parts of song.

Saya sangat suka menggunakan GP 6 sebagai hobby non komersiil , tapi sayangnya GP 6 masih sangat banyak kelemahannya dan terlalu dibatasi kurang leluasa , sehingga cukup menyulitkan untuk penulisan maupun untuk konversi dari tabulasi Midi yang sudah ada ke GP 6 seringkali oktaf terpotong atau tak terbaca. Untuk itulah saya sangat mengharapkan kehadiran GP 7 atau update dari dari GP 6 tentu dengan innovasi serta kemudahan penggunaannya , perlu diketahui bahwa saya menulis midi dengan menggunakan Music Writer Profesional Voyetra yang sangat mudah dan menyenangkan untuk menulis lagu.

Demikian alasan saya, semoga diperhatikan dan terima kasih. Fretboard visualization tool — assign scales to different places in the score, just as with chord diagrams, so that they can be mapped to a chord progression. It is of limited use to be able look at one scale all the time. It is too small to see properly when practicing.

I have recently bought a Fretlight guitar, and it would be nothing short of sensational if the same functionality is available for the Fretlight as for the fretboard visualization. Chord diagrams — ability to store a library of chords — option to reorder appearance of chords in user-created list. Graphics export — user-defined resolution of png files, or at least a higher resolution than what is currently used.

Keyboard shortcuts — select note s on next beat. When entering left hand fingering it is tedious to use both the left-right and up-down cursor keys. Left-hand fingering — remove left-hand fingerings from selection used to be in GP5 — perhaps add a few options to the style dialog. Designer — stretch beats inside bar. If you have a bar with only a few 16th notes in it, they are squeezed tightly together, even if you make the bar itself quite large.

Chromatic notation — not too many guitarists would be interested in this Guitar Pro is not far away from being capable of competing with notation software such as Finale and Sibelius so might appeal to a different audience if you are willing to consider this please send me an email so that I can elaborate. I write music with lots of layers and swap instruments using the mixable within a track constantly so when i finally upgraded to GP6 i was extremely dissapointed to find out all my.

Maybe even have a practice icon on the left where guitarists can look at scale charts and chord charts, and read guitar instructionals. Have the ability to create and store their own practice sessions. As a high school music teacher I use this on a daily basis. Also it would be great to be able to input to either the stave or tab.

Another feature I can think of is OCR for music: using my scanner, you can read music scores. I would like the notation size to be configurable. I am legally blind and would love to be able to make the music notes big so I can see them. I would like to have personalised style font not just classical and jazz.

Best regards. It would be awesome if there was a feature which allows the guitar to switch to distortion on some parts and then back to clean again. It is already possible in GP6. The software in gp6 is beast! Maybe work on improving the palm mute sounds, and allow for VST effects support for things like Amplitube and Freeamp for those of us who lack the funds for major purchases.

Another feature needed is custom drum kits. As in pick this snare and that crash, rather then pick Custom, or Fab, or Oak. Sorry, my english is bad. Is there a way to set up a partial capo tab for instance seventh fret capo with b and e open? Play delete and again…. Correct the legatos, the distortion with chords and Well is especial for guitar but RSE for wind and bowed strings will be awesome. Track mirroring. The UI I invision is a bit like multi-voice editing. For band practise and to embed true voice lyrics into a tab, it would be great to have something like a streaming audio track, next to the currently available tracks.

On this new track type, MP3 or Wav files should play with the tab, just as any other track. Solo, Mute, Volume and Pan would apply as normal. Other track atributes do not really make sense. When using GP for band or instrument practice, a nice feature would be to store a collection of scores into something like a project or session. This would allow easy restoring of previous sessions or sessions that are used frequently, like all songs in a concert or all practise songs for a student.

Somewhat diverting from the GP concept, but still quite useful, would be something like song structuring. So, instead of having to copy and paste a drum or bass loop n-times while the other instruments are playing, an independent repeat on a track would prevent this. This also greatly enhances the readability of scores when printed. Drawback would be that the scores of the different instruments no longer neatly line up when displayed together. It would be good to have a simple strumming pattern feature instead of having to key in chords in riff form.

Adding mp3 track and sync to the tabs 2. Vibrato sync with tempo 8th,16th, Triplets. Better orchestral percussion sounds 3. More effects cymbals for those of us who want to add in parts similar to that of Mike Portnoy, Mike Mangini, and Gavin Harrison 4. More cymbals in general 5. Better ride and ride bell sounds.


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Dear Arobas Music Team! When you read this entry please think about a possibility to change the keyboard command! Guitar pro 7 should have keyboard shortcut for drum track like guitar pro 5. Also there should be the option to change piano instruments from grand staff to regular treble clef and keyboard shortcuts for those as well. The bass in GP6 is too loud, that should addressed, the workflow is very slow in GP6 do to lack of keyboard shortcuts.

You should be able to use Midi and RSE at the same time, just like in guitar pro 5. Why not add the ability to play along with the tab and have the program grade you, similar to Rocksmith? It seems like a logical next step and could be an entirely new software or optional add on. I think a lot of people would buy something like that for the PC. Hi, my suggestion would be for you guys to make a feature that would convert GP. Giving the fact that I cannot play a vast majority, if not all, of the orchestral instruments featured in GP6, it is rather difficult to publish my work, or even find someone that plays them.

Scalable and printable guitar neck Ex if you want to print a scales for practice 2, Better drum writing 3, printable chord book and the amility to order the chord as you want Ex if you want to have specific chord on paper for practice 4, tab book. It would be awesome if 7-string guitar tracks could be converted to down-tuned 6-string guitar tracks.

With custom hotkeys we would never have that problem and could adjust to our preference. Some sort of interface to visually see the order of the sections of a song and rearrange those on the fly. This would really help in writing songs and would save a lot of time and effort trying to copy and paste everything to change song structures. Additionally, a feature that someone else already described would go great with this:.

Also, the feature that someone else mentioned earlier, the ability to program effects chain changes into a track would greatly reduce the amount of tracks necessary to properly reproduce a song. That would be best, even over the competition. The ability to import and export music XML would be nice. It worked in GP5 — will it work in GP7? For your information, Guitar Pro 6. This update will be available in few weeks BETA 6. Many people said this already, but I found myself needing a true way to mix mp3 files with the other instruments. Just to create an example, sometimes I want to add some soundscapes to my songs and I would like to be able to paste mp3 mock-soundscapes just to listen to how it may blend with the rest of the song before recording.

Another thing that would make the software better for ME at least is more playing modifiers for drums choke cymbals, double stops , similar to the guitar ones like bend, hammer-ons. Double stops are hard to simulate, and choke cymbals are impossible. Thanks for your work on this software! It made my songwriting more dynamic and faster than before!

Make string number and tuning completely customizable, so we have everything from an exotic African one-string violin to thirteen string lapsteel or xx-string sitar. None of the big does that Finale, Sibelius. It will need to be configurable, because the pedals steel configurations are still highly individual. Most important: Better editor performance.

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Even compared to v5 the editor of v6 gets really slow when you edit large tabs. When you edit a tab with some complex bars and 7 or 8 tracks, it takes several seconds before v6 answers again and shows any result. That lack of performance is really really annoying. But not half as important as getting the program to work faster! First, sorry for any grammar mistakes in advance! In my opinion only a few stuff is missing from GP. But some of them are big ones. To be able to use different time signatures for the instruments within the same section.

Few other sounds for drums, like: secondary snare, secondary hihat, bells with high, low, and mid tone. Plus individual EQ-ing for every sound, not just full instruments. That would be extremely helpful I think! If we could color one part, and use a different color for the next as a background color , It would be a lot easier to navigate within a song. To be able to use custom names for the sections as well, not just the provided ones. A little more usability for drummers. For example: creating a good buzz-roll is almost impossible in GP. Sounds really bad!

To be able to change the size of the notations. Independent manipulation of a note, even if you have more sounds at the same time. Like: Drum: playing ghost notes, and you reach a point, where you hit the bass drum and the crash, BUT still hitting a ghost note with your left. If you change the dynamics to — for example — forte-forte, your ghost note will be loud too.

Thats not good. To me, it seemed as though gp6 added so much but took away the simplicity and accuracy of gp5. It takes longer to do basic functions and I can never get the guitars to sound good. Just incorporate better structure, better MIDI, option for plug in transcription. It would be cool if there was a simple way to send my tabs to friends and in cloud a player so they can see it and here it as I do on guitar Pro 6. I really would appreciate more work on the strings. The sound of them is terrible, especially the sound of the cello.

In addition the sound is displaced an octave to the actual notation! I know its called GUITAR pro and im quite exotic with my 6-string electric cello, but some features, like not limiting options to instruments in general would be awesome. Add support for MusicXML 3.

I would love to see better page view and printed page layouts in terms of system ie, staff or track layout. As it is now, on multitrack view we can only select to see a staff or not. Most sheet music is printed in this way as it wastes space, paper and just looks bad to have for example track 2 with empty bars in a bar song. So it seems that MusicXML is not standardized yet…. Guitar pro must professional notation program, not amateur tab program. GP sheet view must be pro level, notehead, stems, beams, staves etc.. The great convenience for the composer.

We have recording software if we wanted to such perfect sound but it would me much better if GP7 was more tablature or notation friendly, fast and easy to use rather than more effects, amps or cabinet stuff. GP5 was much better than GP6 in smaller ways. I know you can transpose from one key to another but if you could take a guitar track and transpose to a mandolin or octave mandolin or banjo ukelele or any of other various stringed instruments or transpose from those instruments back to guitar, that would be absolutely fantastic.

Musicians needs perfect notation program, you know. A program better than pencil and paper sometimes. Gp multivoice option is not perfect. You should review or re-construction this option. Very important thing is that stabil and versatil notation or tab symbols writing and reading. Sound is the fourth necessity in this program.

As a lefty the chord diagrams are almost useless to me. Please add the ability to display and print the chord diagrams left-handed. It would be usefull a plugin to a count down before the music starts. Also features like guitar rig has, such as plugin your guitar in the pc and back to the amplifier to make different pedal sounds. The way that the bars display on the screen needs to be better. This makes it difficult to play with the music accurately.

I think there should be a straight forward normal distortion tone, because allthough there are lots of costumization options for your guitar tone in guitar pro 6, that just makes it more difficult to create a nice tone, were as in guitar pro 5 that standard distortion seemed to go well with everything. Also, when using tabs with guitar pro 6 that were created in guitar pro 5, the tempo is not the same in both versions, and the drums soud seemn out of sync. Have a track for backing music.

We use GP to learn songs. Right now its terrible listening to a reed instrument do the vocals. Instead of a band using sheet music or memory, it would be on-screen, real time. Please keep the program simple to use. Progression sucks because its not convenient as GP6. I have a couple of requests that I will post here. First off I love using this software for writing stuff. I can get some really great sounds and a really great idea for how a song will sound.

If you could have a plug-in option to use rewire programs like Kontakt by Native Instruments or even something crazy like Reason although I think that would be tougher given the way Reason operates as a rewire aplication. I really hope you give this idea a shot. Would be nice if you would upgrade the RSE engine, because now some string notes sound a little flat.

More instruments would be nice too…. Better MIDI import. This would save me a ton of time. Also, including ties in the rhythm notation slashes would be very beneficial. Thanks for listening to users! Try this. Hello Mamazi, thanks for your feedback. Hello, the fingering for left and right hand should not be displayed at the same place.

A good working scan option would be a nice addition. But here are my recommendations! I would love it if you could get the old drum notation system in addition to the newest one. With also the option of choosing a different dynamic for every note on the drum track, just like previous versions. Also, if you could get the option for generally see or hide the score or tab just like you could do from the view menu, rather then having to go through each and every channel and do it separably. I would like jukebox options like Band in a box. Copy paste some files to jukebox folder and after appear ing files in the jukebox on GP menu , all songs in there what I want play.

I think good idea. Noob Alert Here!! Then there is the desire to write new music utilizing the exotic styles of a Mr. Arobas is not in competition with the most powerful DAWs. Barring a perfect way to export to Logic, Sibelius, Finale, Cubase and ProTools, to name a few is my single, largest concern, unless I have missed something in my admittedly freshman studies and work. If export is not possible then there should indeed be a way to either 1 : use within GP the strengths of the expensive DAWs, or conversely 2 : use GP reliably within our DAWs of choice, the we are stuck in a nice, but limited proprietary situation.

Please excuse my ignorance in these comments. I am still just getting my feet wet once more after nearly 30 years since my initial hardware foray! I invite any an all corrections, suggestions, comments of a constructive nature! Thank you for taking the time to read, snort with laughter, or have a light bulb turn on over your heads! In the newspaper trade before I was old enough to work, a story would be indicated as finished by adding the following:. Gp is terribyl bugy softwareon Win7.

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After crashing can not re open file and appearing 0 KB. Really awful system. Gp7 you must be professional software. Let us know. The deadline for comments was September 11th last year. There are now comments…. Nearly one year ago you stopped paying attention to suggestions. Any deadline for delivery of GP7?

It would be nice to have a 64bit version. Also, a good improvement would be to add some good symphonic and ethinc soundbanks. All of them classic and medieval instruments would be a nice addition to your product. I would like a custom setting for the tempo adustment. At the moment you have. It would be good if it could be customizable to whatever you want say. Also the repitition goes up to 99 at the moment. I would like it to go upto Also, the ability to add more than a single of text above or below the score. I think that GP is probably the best music editor for guitar and other instruments!

I have been using Guitarpro ever since I learnt bass; and I have been using it since Guitarpro 5. First of all, I would just like to say that although guitarpro 6 is visually more attractive, I thought the transition from 5 to 6 was a little difficult. I do prefer writing drums the guitarpro 5 ways. I mean guitarpro 6 almost felt a little like mixcraft. For Guitar Pro 7, or an update for 6, you guys should really implement a sort of drum machine as an alternative way to add drum grooves to your tab.

Where it would be a lot faster and easier to use rather than tapping in each note. There should be a system where there are pre made bars of drum grooves and fills that can be customised and dragged into each bar, and displayed in the usual drum notation. But also keeping the current system for totally creative control. This would be awesome if there was this drop and drag drum beat bank could possibly even work for other instruments?

Would be so much faster to create tracks. I loathe tabbing drums and this would be a musical life saver for many. It would be cool to not have to indicate what note you want to use. Basically, turn all the measures into a big ruler that would enable indication to place each note accordingly. Or maybe just some different variations of actual singing that can be altered. I feel very limited by the new system. In GP5 i could bend the note any way I wanted.

I would also like to have an unlimited number of strings when tabbing. Many people now have string guitars. It would also make writing piano much easier. First, thank you for the post and for making GP in the first place. I liked some of the new features in GP6, but simplicity and easiness to use are sadly missed in this version.

Tabbing stuff takes a lot of time now while with GP4, I could tab whatever comes into my mind in a matter of minutes — and I mean everything, the rhythm tracks, the arrangements, the drum lines with all necessary fills included, etc. GP6 1. Is it possible to use guitar pro for creating exercises with Tabs, Text and photos. They should be available both on a per track basis and in the mixer for wrapping up the project. And definitely time signature changes mid song are critical. Also how about intentional acoustic feedback, and even the sounds that come from switching back and forth among pickups as I try to channel a bit of Hendrix would be very cool along with the smell of Ronson lighter fluid and charring guitars…joke!!

I personally make Bob Dylan sound amazingly great…so the ability to link human singing directly to the lyrics—I have no problem converting lyrics in-to foe-net-ick form-at until it sounds human—would be one hell of an astounding solution for those of us who hate hearing bassoons where the lyrics go! Yes, I know there are way too many ways to play way too many kinds of harmonicas, but how cool would it be to be able to include a Butterfield song, with Mike Bloomfield on guitar, Paul on harp, Geoff Muldaur on vocals….

I would die a happy old guy! Punching in 36, 38, 36, 36, 38 is music to my fingers, since I, and many with me, started writing tabs in GP2. To be able to use a midi control surface with Guitar Rig and record the mixing patterns. To add an API for plugins so we, the programmers that use your software, can add plugins that enhance the software with functions that are missing.

Audio tracks and to be able to synchronize with the rest of the tracks. More elaborated effects, more control in whammy bar, scrapes … 5. To be able to rearrange a song bought from MySongBook. Without this MySongBook is utterly useless. I really find GP6 to be an improvement over GP5, though the transition took some time.

Some suggestions for GP This is something I know that many would like to see. Personally I would pay extra for that option. VST Input would be the best feature that you could add to this program. Otherwise it works perfectly how it is for me. So my suggestions will be centered on sound production. Ability to change the accent level. Although the string can be changed manually, faster automated ways of doing this would be helpful. What I currently do is erase all the notes in one file that I like the settings, then paste the notes to make a new song.

It could be cleaner to do it the other way around. That means keeping notes on the same string. Might be difficult though. Export to other audio formats other than WAV. FLAC for example. MP3 might be nice too, but not so helpful for a DAW-ist. I would like to see my drum beat played on a digital drum set. For instance, how the music is played on a keyboard for piano pieces while the track is playing, the same for drums.

Not sure if this has been mentioned. One thing RSE never got right was low tunings, lots of us use this for tabbing out technical metal songs with tunings way lower than standard…not to mention 8 strings tunings that are more prevalent. Also a feature for RSE pretty pretty please…more options to define the tone of the sample, better EQ options, effects and amp models! How about adding an external sound card to it so you can plug in and record what you what you are playing and it write it to sheet.

A video training series for newbies! Maybe even one for intermediates and advanced users. Video training series would really help. It would be a nice value add for your purchased users — tie access to it into your account somehow. Hello Drew, have you checked out our video tutorial program yet? There are so many things if you ask me. So here are the ideas which are almost same as mine. Hello, I would Like to be able to export lilypond files. Also I would like more desktop publishing type control over font sizes,staffs. Been using Guitar Pro 4, 5, 6… first your software is awesome, thank you for making it!

However, here are my main concerns:. Midi support is less than good in the Gp6 compare to GP5… problem is.. I was about to switch to sibelius just to get decent midi support and also VST support. Problem is, sibelius is completely retarded when it comes to guitar tablature. This is really time consuming. I find it stupid to need another track just for a different sound… and losing a midi channel for that. Fix it please! I just thought of this idea and posting here unaware as to if its already been suggested, but here goes.

Sitting here trying to tab our original compositions is fun, the interface has really nice flow, and if your fluent enough in music, it can be picked up fairly quickly. I find it especially annoying with drum tabs. For myself not being an actual drummer, I produce the sound I want in my head and then lay it out.

If it sounds like something is missing or needs to be changed, I make the necessary adjustments. If it seems to be missing one kick in there anywhere, the bar needs to be scrapped and restarted. Maybe an 8th or 16th-note grid in the bend window so you can actually specify where the bend lands in the staff? Right now in GP6, there is no way to guess where the bend will release visually, and when using bends with tied notes it can get really screwed up. I can adjust how it sounds but not how it looks.

Example in fingerstyle, use downward for bass and upward for melody. I am really happy with the improvements in GP6 over GP5. Especially the new options for score layout, as I write material for my guitar teaching. These functions are easy to use, and works very well. I would be glad to see even more improvements in this section.

For example:. Or also, between the tab and the standard notation. For example write a few bars of tab, then maybe some text or just open space, then perhaps more tab on the same page. This way I could write an intro, a verse, a refrain and ending of many songs on maybe one page or two. Then I could just write the form of the song in text.

I could of course only use Sibelius for this type of stuff, but as other people pointed out, the tabbing function is so superior in GP, and really fast to use. So with a few adjustments, I could also make proper scores in GP, I would love that. This would be especially useful for programming drums. It would be epic if you could make it the same as guitar pro 5 so when you type in 0 on the 6th string, it plays the E note right as you press 0, not only when you press play.

I have been using guitar pro 6 since its release and i love it.. Please improve the sound banks for strings and other classical instruments. The RSE for orchestral does not match. For example, strings and brass do not go well together because they are pitching differently. On the notation side, have eight notes beam together even when you have a grace note in between them. The acoustic guitar sounds in Guitar Pro are not that good, as you might expect.

It would be a good idea, to add some real good samples to the acoustic guitars. You might think of Olsen, Ryan, Lowden or a few guitar from that category. It would make the files sound much better, I believe. Better strings and flutes and a Lilypond export to fine tune the printing. Thank you for the good job and for supporting Linux! Better RSE Soundbanks especially, palm muted guitars, the orchestral soundbanks are very inconsistent in quality.

The volume indicator on the tracks could also be a solid colour green that changes when hitting high levels. I would like to enter notes without the TABS, only with the standard notation. The software should allow me to NOT accept suggestions on which string and fret I put the note. Thus, the software becomes more flexible to use.

Midi recording! Bring back the old drum tab numbering system. Just tested Guitar Pro There seems to be a bug or something.. When inputting notes there is no sound, i have to press the play button to hear it. This has to be fixed. My opinion is very simple, make it free, otherwise why should i need a new guitar rpo, i dont need another version to spend the money on, the older versions are enough. Usefull would be have a bank with patterns. It would be usefull say where switch what stompbox.

Techniques — it would be usefull to say play this part with pickup and this using fingers. Very excited about the prospect of ANY enhancements to my favorite guitar-related software, which I am already totally obsessed with. But since you asked. Also ability to lock fretboard to screen above or below tab during playback in full-screen mode. GP 6 was a breakthrough!

Seriously, I think you made notes redundant. I would really like some version control, similar to svn or git but for guitar pro tabs. Especially to highlight the changes made by someone else. Some functions I use very often are very hard to reach and hidden in some menu.

Maybe you should rethink your Infomation architecture. Often I just want to write some riff down, but when i use GP6 it just takes too long. An auto-powerchord function would help a lot. So if i write lets say:. At the moment, rearranging sections of a song is a pain in the arse and requires a lot of copy and pasting, reworking time sigs, automations, etc. Add more pedals, effects and more RSE instruments. The GP6 has quite improved and has a lot of varieties.

We need to have real fret-like effect sound aside of the grace notes and need improvement for the dead note effect and palm muting.

I would seriously suggest, that you do not try to invent a wheel again, so to speak. Since there already are very good VSTis around. Some even for free. I would like to have a support for GP2 files. I think it still goes unsupported, which is sad, since I have much material in that format also. It would be great to have drum grooves or patterns to speed up drum-programing. Also a touchscreen-friendly interface option, like bigger buttons and a touch note-entry.

And of course vst-support. Fix the bug where the measure number only shows on the first measure upon printing , despite having the option to have the number appear on all measures checked. Ability to play a real guitar into the computer if users have a proper soundcard. GP would calculate and populate the score.

Hello Arobas team! Even when I really like some of the new features in 6 version, when I export to. My drummer, my bassist and myself… we all want the old fashioned drums notation of gp5… and improvements in the orchestral instruments can be really cool to!. I am amazed at the quality of this program. Thankyou so much GP6!!! I should go and try it for myself, but I just wanted to put it out there and see if anyone has any ideas. One weak side is still the lyrics. Preferable unlimited number of lines.

For the last one that would make the lyrics for the sections independant from each others so that you could easy work with one section of the lyrics without alterering the entire lyrics. Same with drums. This absolutely needs to be in the software for this to be a true Guitar Pro program.


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Keep it toggle-able. To recap: basically bring back all the removed features and abilities from GP5. Make it so that if there is something you can do in GP5, it can also be done in GP6 a true improvement of the original software, rather than a complete rebuild with missing parts.