Musique | EPFL Graph Search

vignette|Allégorie de la musique, François Boucher, 1752. vignette|Jean-Sébastien Bach, compositeur de musique baroque. vignette|Jacques Brel, chanteur. vignette|350x350px|Mozart, La Flûte enchantée, première page du manuscrit autographe. vignette|Allégorie évoquant la musique et les instruments.vignette|290x290px|Beethoven, Symphonie , première page du manuscrit autographe. La musique est un art et une activité culturelle consistant à combiner sons et silences au cours du temps. Les paramètres principaux sont le rythme (façon de combiner les sons dans le temps), la hauteur (combinaison dans les fréquences), les nuances et le timbre. Elle est aujourd'hui considérée comme une forme de poésie moderne. La musique donne lieu à des créations (des œuvres d'art créées par des compositeurs), des représentations. Elle utilise généralement certaines règles ou systèmes de composition, des plus simples aux plus complexes (souvent les notes de musique, les gammes et autres). Elle peut utiliser de

Learning music composition with recurrent neural networks

Florian François Colombo

Throughout this thesis, we are interested in modeling music composition. To do so, we study the association of music theory concepts with the learning capabilities of recurrent neural networks. Especially, we explore numerical formalizations of music so that our models operate on data containing useful information to decide how to combine particular rhythms and notes in specified musical contexts. The first part introduces the Deep Artificial Composer, a generative model of monophonic melodies. We present a network architecture that considers the temporal and instantaneous relationships between the pitch and duration of notes. By combining Irish with klezmer melodies to train the Deep Artificial Composer, we study how it reacts to heterogeneous data. Furthermore, we show how to generate melodies containing Irish or Klezmer folk musicâs characteristics from our probabilistic model of notes. The second part presents BachProp, a model for the composition of polyphonic music. We discover how scores of different styles can be generated from models using neural networks. In particular, we train BachProp with Bachâs chorales and the English folk music corpus Nottingham. Also, we verify that generated compositions share specific properties with the original corpus it processes. Especially, our musical novelty statistics show that BachProp generates musical sequences as innovative as those present in the original corpora. Finally, a large audience appreciated the Ada string quartet performances of BachPropâs compositions. Whereas BachProp operates on any music in the usual MIDI format, the BeethovANN algorithm, introduced in the last part, needs the information contained in scores in the MusicXML format. These digital scores allow us to formalize relevant musical characteristics in order to generate music with BeethovANN. Thus, to create new voices that can be played in accompaniment to Beethoven string quartets, we take into account not only the note sequences, but also the harmonic progression, interactions between voices, and the rhythm of each instrument. Eventually, professional musicians misclassified BeethovANN musical phrases for Beethovenâs original compositions.

EPFL2021

Learning and generation of slow sequences: an application to music composition

Florian François Colombo

Human brains can deal with sequences with temporal dependencies on a broad range of timescales, many of which are several order of magnitude longer than neuronal timescales. Here we introduce an artificial intelligence that learns and produces the complex structure of music, a specific type of slow sequence. Our model employs a separation of fundamental features and multi-layer networks of gated recurrent units. We separate the information contained in monophonic melodies into their rhythm and melody features. The model processes these features in parallel while modelling the relation between them, effectively splitting the joint distribution over note duration and pitch into conditional probabilities. Using such an approach, we were able to automatically learn the temporal dependencies inherent of large corpora of Irish folk songs. We could use the extracted structural rules to generate interesting complete melodies or suggest possible continuations of melody fragments that are coherent with the characteristics of the fragments themselves.

2016

Music Learning with Long Short Term Memory Networks

Florian François Colombo

Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the highly complicated works of J.S. Bach. However, how our brain is able to store and produce these very long temporal sequences is still an open question. Long short-term memory (LSTM) artificial neural networks have been shown to be efficient in sequence learning tasks thanks to their inherent ability to bridge long time lags between input events and their target signals. Here, I investigate the possibility of training LSTM networks to learn and reproduce musical sequences and eventually better understand some of the mechanisms neural networks deploy to learn and compose long time scale structures. To be able to learn music with LSTM networks requires representing musical sequences in these networks. The musical representation developed for this work is inspired by the tonotopic representation of sounds in the auditory system. It is shown that LSTM networks are able to learn each note transitions of the monophonic and polyphonic versions of a simple song using a particular network architecture where both input and output of LSTM networks are musical notes in the developed network representation. However, this architecture for LSTM networks fail to learn longer and more complex musical sequences (e.g. the J.S. Bach cello suites). To solve this problem, I introduce the separation of time scales model, which consists in two connected LSTM networks, operating on different time scales. On one hand, trained slow time scale LSTM networks produce transitions between unique identifiers of musical patterns, which resemble a compressed memory of the pattern akin to neural memory. This gives the long time structure of music. On the other hand, trained fast time scale LSTM networks are producing the note-to-note transitions of each musical patterns. The latter receives as additional inputs the identifiers from slow time scale networks, akin to feed- forward input from memory regions of the brain. These unique identifiers bias fast time scale LSTM networks toward the production of the corresponding musical pattern. The most efficient identifiers of musical patterns are found to be a representation of how similar patterns are from one another. Finally, when unlearned pattern identifiers are given to trained fast time scale networks, novel musical patterns are created from the learned production rules. I show that the introduction of a separation of time scales greatly improves the capacity of LSTM networks to learn a larger body of musical sequences. Finally, I demonstrate that previously unseen input biases can be used to induce the network into the generation of new musical sequences, akin but not similar to known patterns. This presents a possible first step towards the generalization of previously learnt musical knowledge to the creation and composition of new music by artificial neural networks.

2015

Learning music composition with recurrent neural networks

Florian François Colombo

EPFL2021

Music Learning with Long Short Term Memory Networks

Florian François Colombo

2015