Music generation has generally been focused on either creating scores or interpreting them. We discuss differences between these two problems and propose that, in fact, it may be valuable to work in the space of direct performance generation: jointly predicting the notes and also their expressive timing and dynamics. We consider the significance and qualities of the dataset needed for this. Having identified both a problem domain and characteristics of an appropriate dataset, we show an LSTM-based recurrent network model that subjectively performs quite well on this task. Critically, we provide generated examples. We also include feedback from professional composers and musicians about some of these examples.
Perhaps it is precisely because music is so often perceived as a profoundly human endeavour that there has also been, in parallel, an ongoing fascination with automating its creation. This fascination long predates notions such as the Turing test (ostensibly for discriminating automation of the most human behaviour), and has spawned a range of efforts: from attempts at the formalization of unambiguously strict rules of composition to incorporation of complete random chance into scores and performances. The use of rules exemplifies the algorithmic (and largely deterministic) approach to music generation, one that is interesting and outside the scope of the current work; for background on this we refer the reader, for example, to the text by Nierhaus @cite_1 . Our present work, on the other hand, lies in a part of the spectrum that incorporates probability and sampling.
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Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (, 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation. Machine learning models of music typically break down the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. We explore the use of blocked Gibbs sampling as an analogue to the human approach, and introduce Coconet, a convolutional neural network in the NADE family of generative models. Despite ostensibly sampling from the same distribution as the NADE ancestral sampling procedure, we find that a blocked Gibbs approach significantly improves sample quality. We provide evidence that this is due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from (2014) yields better samples than ancestral sampling. We demonstrate the versatility of our method on unconditioned polyphonic music generation. The temporal structure of music is essential for the cognitive processes related to the emotions expressed in music. However, such temporal information is often disregarded in typical Music Information Retrieval modeling tasks of predicting higher-level cognitive or semantic aspects of music such as emotions, genre, and similarity. This paper addresses the specific hypothesis whether temporal information is essential for predicting expressed emotions in music, as a prototypical example of a cognitive aspect of music. We propose to test this hypothesis using a novel processing pipeline: 1) Extracting audio features for each track resulting in a multivariate "feature time series". 2) Using generative models to represent these time series (acquiring a complete track representation). Specifically, we explore the Gaussian Mixture model, Vector Quantization, Autoregressive model, Markov and Hidden Markov models. 3) Utilizing the generative models in a discriminative setting by selecting the Probability Product Kernel as the natural kernel for all considered track representations. We evaluate the representations using a kernel based model specifically extended to support the robust two-alternative forced choice self-report paradigm, used for eliciting expressed emotions in music. The methods are evaluated using two data sets and show increased predictive performance using temporal information, thus supporting the overall hypothesis. Automatic music generation systems have gained in popularity and sophistication as advances in cloud computing have enabled large-scale complex computations such as deep models and optimization algorithms on personal devices. Yet, they still face an important challenge, that of long-term structure, which is key to conveying a sense of musical coherence. We present the MorpheuS music generation system designed to tackle this problem. MorpheuS' novel framework has the ability to generate polyphonic pieces with a given tension profile and long- and short-term repeated pattern structures. A mathematical model for tonal tension quantifies the tension profile and state-of-the-art pattern detection algorithms extract repeated patterns in a template piece. An efficient optimization metaheuristic, variable neighborhood search, generates music by assigning pitches that best fit the prescribed tension profile to the template rhythm while hard constraining long-term structure through the detected patterns. This ability to generate affective music with specific tension profile and long-term structure is particularly useful in a game or film music context. Music generated by the MorpheuS system has been performed live in concerts. Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds. Machine-learning techniques have been recently used with spectacular results to generate artefacts such as music or text. However, these techniques are still unable to capture and generate artefacts that are convincingly structured. In this paper we present an approach to generate structured musical sequences. We introduce a mechanism for sampling efficiently variations of musical sequences. Given a input sequence and a statistical model, this mechanism samples a set of sequences whose distance to the input sequence is approximately within specified bounds. This mechanism is implemented as an extension of belief propagation, and uses local fields to bias the generation. We show experimentally that sampled sequences are indeed closely correlated to the standard musical similarity measure defined by Mongeau and Sankoff. We then show how this mechanism can used to implement composition strategies that enforce arbitrary structure on a musical lead sheet generation problem. A common task in dance, martial arts, animation, and many other movement genres is for the character to move in an innovative and yet stylistically consonant fashion. In this paper, we describe two mechanisms for automating this process and evaluate the results with a Turing Test. Our algorithms use the mathematics of chaos to achieve innovation and simple machine-learning techniques to enforce stylistic consonance. Because our goal is stylistic consonance, we used a Turing Test, rather than standard cross-validation-based approaches, to evaluate the results. This test indicated that the novel dance segments generated by these methods are nearing the quality of human-choreographed routines. The test-takers found the human-choreographed pieces to be more aesthetically pleasing than computer-choreographed pieces, but the computer-generated pieces were judged to be equally plausible and not significantly less graceful. Musical or choreographic variation—one of the most fundamental of compositional techniques—are based upon two phases of work: establishment of a grounding theme and a series of structured departures from that theme. Classic examples are Bach's Goldberg Variations for the keyboard or Balanchine's The Four Temperaments ballet. The properties of dynamical chaos provide unusual mechanisms for achieving these effects. The characteristic patterns of strange attractors can be used to capture the underlying theme of the sequence and chaos's sensitive dependence on initial conditions can be used to produce variations. This intriguing notion was proposed by Diana Dabby in the mid-1990s in the context of music and image (1, 2). Inspired by Dabby's work, we set out to apply some similar ideas to dance. The result was the pair of tools that are described in this paper: CHAOGRAPHER, which produces chaotic variations of keyframed movement sequences, and MOTIONMIND, which extracts the patterns from a corpus of movement sequences and uses those patterns to generates original and yet stylistically consonant movement. These two tools, working together, can produce novel, interesting movement. The core of CHAOGRAPHER's strategy for introducing novelty is to create a mapping that associates a sequence of body positions with a chaotic attractor, and then use that mapping to generate variations. The fixed attractor structure guarantees that the variations will resemble the original in a mathematically precise sense, while sensitive dependence on initial conditions guarantees We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval. Machine improvisation and related style simulation problems usually consider building repre- sentations of time-based media data, such as music, either by explicit coding of rules or applying machine learning methods. Stylistic learning applies such methods to musical sequences in order to capture salient musical features and organize these features into a model. The Stylistic simulation process browses the model in order to generate variant musical sequences that are stylistically consistent with the learned ma- terial. If both the learning process and the simulation process happen in real-time, in an interactive system where the computer “plays” with musicians, then Machine Improvisation is achieved. Improvisation models have to cope with a trade-off between completeness (all the possible patterns and their continuation laws are discovered) and incrementality (the completeness is ensured only asymptotically for infinite sequences). In a previous work we devised a complete and incremental model based on the Factor Oracle Algorithm. In this paper we propose a concurrent constraints model for the Factor Oracle and show how it can be used in a concurrent learning improvisation situation. Our model is based on a non-deterministic concurrent constraint process calculus (NTCC). Such an approach allows the system to respond in a faster and more flexible manner to real-life performance situations. In addition, the declarative nature of constraints greatly simplifies the expansion of the system with improvisation rules at a higher musical level. We also describe the implementation of our model in a NTCC interpreter written in Common Lisp that is capable of real time performance. Research on automatic music generation has seen great progress due to the development of deep neural networks. However, the generation of multi-instrument music of arbitrary genres still remains a challenge. Existing research either works on lead sheets or multi-track piano-rolls found in MIDIs, but both musical notations have their limits. In this work, we propose a new task called lead sheet arrangement to avoid such limits. A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs. Our model can generate lead sheets and their arrangements of eight-bar long. Source code and audio samples of the generated result can be found at the project webpage: https: liuhaumin. github.io LeadsheetArrangement While there is a growing body of work proposing grammars for music, there is little work testing analytical grammars in a generative setting. We explore the process of learning production probabilities for musical grammars from musical corpora and test the results using Kulitta, a recently developed framework for automated composition. To do this, we extend a well-known algorithm for learning production probabilities for context-free grammars (CFGs) to support various musical CFGs as well as an additional category of grammars called probabilistic temporal graph grammars. Music is composed to be emotionally expressive, and emotional associations provide an especially natural domain for indexing and recommendation in today's vast digital music libraries. But such libraries require powerful automated tools, and the development of systems for automatic prediction of musical emotion presents a myriad challenges. The perceptual nature of musical emotion necessitates the collection of data from human subjects. The interpretation of emotion varies between listeners thus each clip needs to be annotated by a distribution of subjects. In addition, the sharing of large music content libraries for the development of such systems, even for academic research, presents complicated legal issues which vary by country. This work presents a new publicly available dataset for music emotion recognition research and a baseline system. In addressing the difficulties of emotion annotation we have turned to crowdsourcing, using Amazon Mechanical Turk, and have developed a two-stage procedure for filtering out poor quality workers. The dataset consists entirely of creative commons music from the Free Music Archive, which as the name suggests, can be shared freely without penalty. The final dataset contains 1000 songs, each annotated by a minimum of 10 subjects, which is larger than many currently available music emotion dataset. Several methods exist for a computer to generate music based on data including Markov chains, recurrent neural networks, recombinancy, and grammars. We explore the use of unit selection and concatenation as a means of generating music using a procedure based on ranking, where, we consider a unit to be a variable length number of measures of music. We first examine whether a unit selection method, that is restricted to a finite size unit library, can be sufficient for encompassing a wide spectrum of music. We do this by developing a deep autoencoder that encodes a musical input and reconstructs the input by selecting from the library. We then describe a generative model that combines a deep structured semantic model (DSSM) with an LSTM to predict the next unit, where units consist of four, two, and one measures of music. We evaluate the generative model using objective metrics including mean rank and accuracy and with a subjective listening test in which expert musicians are asked to complete a forced-choiced ranking task. We compare our model to a note-level generative baseline that consists of a stacked LSTM trained to predict forward by one note. This paper proposes a Bayesian approach for automatic music transcription of polyphonic MIDI signals based on generative modeling of onset occurrences of musical notes. Automatic music transcription involves two subproblems that are interdependent of each other: rhythm recognition and tempo estimation. When we listen to music, we are able to recognize its rhythm and tempo (or beat location) fairly easily even though there is ambiguity in determining the individual note values and tempo. This may be made possible through our empirical knowledge about rhythm patterns and tempo variations that possibly occur in music. To automate the process of recognizing the rhythm and tempo of music, we propose modeling the generative process of a MIDI signal of polyphonic music by combining the sub-process by which a musically natural tempo curve is generated and the sub-process by which a set of note onset positions is generated based on a 2-dimensional rhythm tree structure representation of music, and develop a parameter inference algorithm for the proposed model. We show some of the transcription results obtained with the present method. The issue of communication in music is regarded with some suspicion by music analysts and music theorists for at least two reasons. One is the desire to avoid the pitfalls of a naive and dogmatically intentionalist approach to musical communication in which the composer is the ‘sender’, the listeners are the ‘receivers’, and the work itself is the ‘channel’. The drawbacks of this perspective, exemplified by Cooke (1959), are its rigidly prescriptive tendencies, and its dependence either on loose and unreliable biographical information to establish what the composer ‘really meant’ in a work, or on apparently arbitrary or flimsily supported pronouncements by different commentators as to the intended meaning of a work, all of which tend to deflect attention away from the music itself. A second reason has been the influence of a brand of structuralism which has regarded the work as an object, whose author or composer is irrelevant — a position adopted by Roland Barthes, among others, and encapsulated in the title of his essay “The death of the author” (1977). Because this perspective encourages analysts to explore whatever meanings may be found in a work, without regard for their possible origins in the author’s own mind, it precludes the possibility of regarding a work as a vehicle which conveys meaning from one human ‘sender’ to other human ‘receivers’: the absence of a source removes one of the necessary terms in the communicative chain. The attraction of this approach is that analysts can concentrate on the music itself without having to consider its origins. The approach also highlights the distinction between communication and meaning: the meaning of an object, event or relation is a property that any observer is entitled to pick up or construct for him or herself, without implying any reciprocity between the creator and the receiver of meaning. Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments tracks, with close interaction with one another. Each track has its own temporal dynamics, but collectively they unfold over time interdependently. Lastly, for symbolic domain music generation, the targeted output is sequences of discrete musical events, not continuous values. In this paper, we propose and study three generative adversarial networks (GANs) for symbolic-domain multi-track music generation, using a data set of 127,731 MIDI bars of pop rock music. The three models, which differ in the underlying model assumption and accordingly the network architecture, are referred to as the jamming model, composer model, and hybrid model, respectively. We propose a few intra-track and inter-track objective metrics to examine and compare their generation result, in addition to a subjective evaluation. We show that our models can learn from the noisy MIDI files and generate coherent music of four bars right from scratch (i.e. without human inputs). We also propose extensions of our models to facilitate human-AI cooperative music creation: given the piano track composed by human we can generate four additional tracks in return to accompany it. So far, many of the computational models for rendering music performance have been proposed, but they often consist of many heuristic rules and tend to be complex. It makes difficult to generate and select the useful rules, or perform the optimization of parameters in the rules. In this study, we present a new approach that automatically learns a computational model for rendering music performance with score information as an input and the corresponding real performance data as an output. We use a Gaussian Process (GP) incorporated with a Bayesian Committee Machine to reduce naive GP's heavy computation cost, to learn those input-output relationships. We compared three normalized errors: dynamics, attack time and release time between the real and predicted performance by the trained GP to evaluate our proposed scheme. We evaluated the learning ability and the generalization ability. The results show that the trained GP has an acceptable learning ability for 'known' pieces, but show insufficient generalization ability for 'unknown' pieces, suggesting that the GP can learn the expressive music performance without setting many parameters manually, but the size of the current training dataset is not sufficiently large so as to generalize the training pieces to 'unknown' test pieces. We introduce a model for music generation where melodies are seen as a network of interacting notes. Starting from the principle of maximum entropy we assign to this network a probability distribution, which is learned from an existing musical corpus. We use this model to generate novel musical sequences that mimic the style of the corpus. Our main result is that this model can reproduce high-order patterns despite having a polynomial sample complexity. This is in contrast with the more traditionally used Markov models that have an exponential sample complexity. This paper presents a probabilistic model of temporal structure in music which allows joint inference of tempo, meter and rhythmic pattern. The framework of the model naturally quantifies these three musical concepts in terms of hidden state-variables, allowing resolution of otherwise apparent ambiguities in musical structure. At the heart of the system is a probabilistic model of a hypothetical ‘bar-pointer’ which maps an input signal to one cycle of a latent, periodic rhythmical pattern. The system flexibly accommodates different input signals via two observation models: a Poisson points model for use with MIDI onset data and a Gaussian process model for use with raw audio signals. The discrete state-space permits exact computation of posterior probability distributions for the quantities of interest. Results are presented for both observation models, demonstrating the ability of the system to correctly detect changes in rhythmic pattern and meter, whilst tracking tempo. Music as a form of art is intentionally composed to be emotionally expressive. The emotional features of music are invaluable for music indexing and recommendation. In this paper we present a cross-comparison of automatic emotional analysis of music. We created a public dataset of Creative Commons licensed songs. Using valence and arousal model, the songs were annotated both in terms of the emotions that were expressed by the whole excerpt and dynamically with 1 Hz temporal resolution. Each song received 10 annotations on Amazon Mechanical Turk and the annotations were averaged to form a ground truth. Four different systems from three teams and the organizers were employed to tackle this problem in an open challenge. We compare their performances and discuss the best practices. While the effect of a larger feature set was not very apparent in the static emotion estimation, the combination of a comprehensive feature set and a recurrent neural network that models temporal dependencies has largely outperformed the other proposed methods for dynamic music emotion estimation.