Gaëtan Hadjeres PhD

Gaëtan Hadjeres PhD

Sony Computer Science Laboratories Paris

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.


I present the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input. The originality of our approach is that VarNet is not only capable of handling pre-defined attributes but can also learn the relevant attributes of the dataset by itself. These two settings can be easily combined which makes VarNet applicable for a wide variety of tasks. Further, VarNet has a sound probabilistic interpretation which grants us with a novel way to navigate in the latent spaces as well as means to control how the attributes are learned. We demonstrate experimentally that this model is capable of performing interesting input manipulation and that the learned attributes are relevant and interpretable.