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Dr. Gaëtan Hadjeres

Associate Researcher

music

Putting composers back in the loop

Applying the latest deep learning techniques to music composition is appealing for AI researchers; but for composers, this intrusion of machines in their domain of expertise could be perceived as a threat. This fear of being replaced is legitimate: indeed, many recent generative models for music tend to produce infinite numbers of
scores without the need for human intervention. I think that this behavior is not desirable and that AI algorithms should instead be used by artists as assistants during the compositional process. By creating a fruitful discussion between a composer and the machine, the artist can then focus on the development of their musical ideas and let the AI do the technical parts. Professional composers can benefit from these tools to become more productive and explore uncharted regions of musical creation while amateur musicians can use these innovative tools to express themselves in an intuitive way. By putting composers back in the loop, we will go from automatic music composition to AI-augmented composition and redefine the way people compose music.

2018

Monte Carlo Information Geometry: The dually flat case

Topics:
music
Authors
Nielsen Frank, Gaëtan Hadjeres,

2018.

2018

Machine Learning Research that Matters for Music Creation: A Case Study

Topics:
music
Authors
Sturm Bob, Ben-Tal Oded, Monaghan Una, Collins Nick, Herremans Dorien, Chew Elaine, Gaëtan Hadjeres, Emmanuel Deruty, François Pachet,

Journal of New Music Research, 2018.

2018

Anticipation-RNN: Enforcing Unary Constraints in Sequence Generation, with Application to Interactive Music Generation

Topics:
music
Authors
Gaëtan Hadjeres, Nielsen Frank,

Neural Computing and Applications, special issue on Deep Learning and Music, 2018.

2017

DeepBach: a Steerable Model for Bach Chorales Generation

Topics:
music
Authors
Gaëtan Hadjeres, François Pachet, Nielsen Franck,

Proceedings of the 34th International Conference on Machine Learning, edited by:Doina Precup and Yee Whye Teh, 70, PMLR, International Convention Centre, Sydney, Australia, August, 2017. pp.1362--1371.

2017

GLSR-VAE: Geodesic latent space regularization for variational autoencoder architectures

Topics:
music
Authors
Gaëtan Hadjeres, Nielsen Frank, François Pachet,

2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017), IEEE, 2017. pp.1--7.

2017

Deep learning techniques for music generation-a survey

Topics:
music
Authors
Briot Jean-Pierre, Gaëtan Hadjeres, François Pachet,

2017.

2016

Style Imitation and Chord Invention in Polyphonic Music with Exponential Families

Topics:
music
Authors
Gaëtan Hadjeres, Sakellariou Jason, François Pachet,

September, 2016.