Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Methodological contributions by means of machine learning methods for automatic music generation and classification

189 p.Ikerketa lan honetan bi gai nagusi landu dira: musikaren sorkuntza automatikoa eta sailkapena. Musikaren sorkuntzarako bertso doinuen corpus bat hartu da abiapuntu moduan doinu ulergarri berriak sortzeko gai den metodo bat sortzeko. Doinuei ulergarritasuna hauen barnean dauden errepikapen egiturek ematen dietela suposatu da, eta metodoaren hiru bertsio nagusi aurkeztu dira, bakoitzean errepikapen horien definizio ezberdin bat erabiliz.Musikaren sailkapen automatikoan hiru ataza garatu dira: generoen sailkapena, familia melodikoen taldekatzea eta konposatzaileen identifikazioa. Musikaren errepresentazio ezberdinak erabili dira ataza bakoitzerako, eta ikasketa automatikoko hainbat teknika ere probatu dira, emaitzarik hoberenak zeinek ematen dituen aztertzeko.Gainbegiratutako sailkapenaren alorrean ere binakako sailkapenaren gainean lana egin da, aurretik existitzen zen metodo bat optimizatuz. Hainbat datu baseren gainean probatu da garatutako teknika, baita konposatzaile klasikoen piezen ezaugarriez osatutako datu base batean ere

A computational framework for aesthetical navigation in musical search space

Paper presented at 3rd AISB symposium on computational creativity, AISB 2016, 4-6th April, Sheffield. Abstract. This article addresses aspects of an ongoing project in the generation of artificial Persian (-like) music. Liquid Persian Music software (LPM) is a cellular automata based audio generator. In this paper LPM is discussed from the view point of future potentials of algorithmic composition and creativity. Liquid Persian Music is a creative tool, enabling exploration of emergent audio through new dimensions of music composition. Various configurations of the system produce different voices which resemble musical motives in many respects. Aesthetical measurements are determined by Zipf’s law in an evolutionary environment. Arranging these voices together for producing a musical corpus can be considered as a search problem in the LPM outputs space of musical possibilities. On this account, the issues toward defining the search space for LPM is studied throughout this paper

Mapping Specific Mental Content during Musical Imagery

Humans can mentally represent auditory information without an external stimulus, but the specificity of these internal representations remains unclear. Here, we asked how similar the temporally unfolding neural representations of imagined music are compared to those during the original perceived experience. We also tested whether rhythmic motion can influence the neural representation of music during imagery as during perception. Participants first memorized six 1-min-long instrumental musical pieces with high accuracy. Functional MRI data were collected during: 1) silent imagery of melodies to the beat of a visual metronome; 2) same but while tapping to the beat; and 3) passive listening. During imagery, inter-subject correlation analysis showed that melody-specific temporal response patterns were reinstated in right associative auditory cortices. When tapping accompanied imagery, the melody-specific neural patterns were reinstated in more extensive temporal-lobe regions bilaterally. These results indicate that the specific contents of conscious experience are encoded similarly during imagery and perception in the dynamic activity of auditory cortices. Furthermore, rhythmic motion can enhance the reinstatement of neural patterns associated with the experience of complex sounds, in keeping with models of motor to sensory influences in auditory processing

Measuring Expressive Music Performances: a Performance Science Model using Symbolic Approximation

Music Performance Science (MPS), sometimes termed systematic musicology in Northern Europe, is concerned with designing, testing and applying quantitative measurements to music performances. It has applications in art musics, jazz and other genres. It is least concerned with aesthetic judgements or with ontological considerations of artworks that stand alone from their instantiations in performances. Musicians deliver expressive performances by manipulating multiple, simultaneous variables including, but not limited to: tempo, acceleration and deceleration, dynamics, rates of change of dynamic levels, intonation and articulation. There are significant complexities when handling multivariate music datasets of significant scale. A critical issue in analyzing any types of large datasets is the likelihood of detecting meaningless relationships the more dimensions are included. One possible choice is to create algorithms that address both volume and complexity. Another, and the approach chosen here, is to apply techniques that reduce both the dimensionality and numerosity of the music datasets while assuring the statistical significance of results. This dissertation describes a flexible computational model, based on symbolic approximation of timeseries, that can extract time-related characteristics of music performances to generate performance fingerprints (dissimilarities from an ‘average performance’) to be used for comparative purposes. The model is applied to recordings of Arnold Schoenberg’s Phantasy for Violin with Piano Accompaniment, Opus 47 (1949), having initially been validated on Chopin Mazurkas.1 The results are subsequently used to test hypotheses about evolution in performance styles of the Phantasy since its composition. It is hoped that further research will examine other works and types of music in order to improve this model and make it useful to other music researchers. In addition to its benefits for performance analysis, it is suggested that the model has clear applications at least in music fraud detection, Music Information Retrieval (MIR) and in pedagogical applications for music education

Methodological contributions by means of machine learning methods for automatic music generation and classification

189 p.Ikerketa lan honetan bi gai nagusi landu dira: musikaren sorkuntza automatikoa eta sailkapena. Musikaren sorkuntzarako bertso doinuen corpus bat hartu da abiapuntu moduan doinu ulergarri berriak sortzeko gai den metodo bat sortzeko. Doinuei ulergarritasuna hauen barnean dauden errepikapen egiturek ematen dietela suposatu da, eta metodoaren hiru bertsio nagusi aurkeztu dira, bakoitzean errepikapen horien definizio ezberdin bat erabiliz.Musikaren sailkapen automatikoan hiru ataza garatu dira: generoen sailkapena, familia melodikoen taldekatzea eta konposatzaileen identifikazioa. Musikaren errepresentazio ezberdinak erabili dira ataza bakoitzerako, eta ikasketa automatikoko hainbat teknika ere probatu dira, emaitzarik hoberenak zeinek ematen dituen aztertzeko.Gainbegiratutako sailkapenaren alorrean ere binakako sailkapenaren gainean lana egin da, aurretik existitzen zen metodo bat optimizatuz. Hainbat datu baseren gainean probatu da garatutako teknika, baita konposatzaile klasikoen piezen ezaugarriez osatutako datu base batean ere

The Role of a Polyrhythm’s Pitch Interval in Music-Dependent Memory

When listening to music, humans can easily and often automatically assess the perceptual similarity of different moments in music. However, it is difficult to rigorously define the way in which we determine exactly how similar we find to moments to be. This problem has driven inquiry in music cognition, musicology, and music theory alike, but previous results have depended on behaviorally mediated responses and/or recursive analytic strategies by music scholars. The present work employs the context-dependent memory paradigm as a novel way to investigate the extent to which listeners consider two musical examples to be similar. After incidentally learning words while listening to a 5:4 polyrhythm forming a perfect fifth, participants could hear no sound or the polyrhythm at a different pitch interval during a surprise test of recall. Between-subjects comparisons found no effect of the actual sound context at test on recall; however, participants who reported being in the same sound context did recall significantly more words than others. Interactions between actual and reported sound context were not accounted for by musical experience or other participant factors, and reported sound context was more often incompatible than compatible with actual sound context. Contributions to mental context theory and the boundaries of conclusions about musical features are discussed

A Functional Taxonomy of Music Generation Systems

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.Comment: survey, music generation, taxonomy, functional survey, survey, automatic composition, algorithmic compositio