Author name cluster

François Pachet

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5 papers

2 author rows

ICML Conference 2017 Conference Paper

DeepBach: a Steerable Model for Bach Chorales Generation

Gaëtan Hadjeres
François Pachet
Frank Nielsen

This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces. We claim that, after being trained on the chorale harmonizations by Johann Sebastian Bach, our model is capable of generating highly convincing chorales in the style of Bach. DeepBach’s strength comes from the use of pseudo-Gibbs sampling coupled with an adapted representation of musical data. This is in contrast with many automatic music composition approaches which tend to compose music sequentially. Our model is also steerable in the sense that a user can constrain the generation by imposing positional constraints such as notes, rhythms or cadences in the generated score. We also provide a plugin on top of the MuseScore music editor making the interaction with DeepBach easy to use.

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TIST Journal 2016 Journal Article

A Joyful Ode to Automatic Orchestration

François Pachet

Most works in automatic music generation have addressed so far specific tasks. Such a reductionist approach has been extremely successful and some of these tasks have been solved once and for all. However, few works have addressed the issue of generating automatically fully fledged music material, of human-level quality. In this article, we report on a specific experiment in holistic music generation: the reorchestration of Beethoven’s Ode to Joy, the European anthem, in seven styles. These reorchestrations were produced with algorithms developed in the Flow Machines project and within a short time frame. We stress the benefits of having had such a challenging and unifying goal, and the interesting problems and challenges it raised along the way.

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AAAI Conference 2014 Conference Paper

Avoiding Plagiarism in Markov Sequence Generation

Alexandre Papadopoulos
Pierre Roy
François Pachet

Markov processes are widely used to generate sequences that imitate a given style, using random walk. Random walk generates sequences by iteratively concatenating states to prefixes of length equal or less than the given Markov order. However, at higher orders, Markov chains tend to replicate chunks of the corpus with a size possibly higher than the order, a primary form of plagiarism. The Markov order defines a maximum length for training but not for generation. In the framework of constraint satisfaction (CSP), we introduce MAXORDER. This global constraint ensures that generated sequences do not include chunks larger than a given maximum order. We exhibit an automaton that recognises the solution set, with a size linear in the size of the corpus. We propose a linear-time procedure to generate this automaton from a corpus and a given max order. We then use this automaton to achieve generalised arc consistency for the MAXORDER constraint, holding on a sequence of size n, in O(n. T) time, where T is the size of the automaton. We illustrate our approach by generating text sequences from text corpora with a maximum order guarantee, effectively controlling plagiarism.

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ECAI Conference 2014 Conference Paper

Imitative Leadsheet Generation with User Constraints

François Pachet
Pierre Roy

We introduce the problem of generating musical leadsheets, i. e. a melody with chord labels, in the style of an arbitrary composer, that satisfy arbitrary user constraints. The problem is justified by the very nature of musical creativity, as many composers create music precisely by imitating a given style to which they add their own constraints. We propose a solution of this problem by formulating it as a Markov constraint problem. Markov constraints enable users to create stylistically imitative leadsheets that satisfy a large palette of constraints. We show that generated leadsheets are stylistically consistent by reclassifying them using Markov classifiers.

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ECAI Conference 2012 Conference Paper

Markov Constraints for Generating Lyrics with Style

Gabriele Barbieri
François Pachet
Pierre Roy
Mirko Degli Esposti

We address the issue of generating texts in the style of an existing author, that also satisfy structural constraints imposed by the genre of the text. We focus on song lyrics, for which structural constraints are well-defined: rhyme and meter. Although Markov processes are known to be suitable for representing style, they are difficult to control in order to satisfy non-local properties, such as structural constraints, that require long distance modeling. We show that the framework of Constrained Markov Processes allows us to precisely generate texts that are consistent with a corpus, while being controllable in terms of rhymes and meter, a result that no other technique, to our knowledge, could achieve to date. Controlled Markov processes consist in reformulating Markov processes in the context of constraint satisfaction. We describe how to represent stylistic and structural properties in terms of constraints in this framework and we provide an evaluation of our method by comparing it to both pure Markov and pure constraint-based approaches. We show how this approach can be used for the semi-automatic generation of lyrics in the style of a popular author that has the same structure as an existing song.

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