  Recent studies show the ability of unsupervised models to learn invertible
audio representations using Auto-Encoders. They enable high-quality sound
synthesis but a limited control since the latent spaces do not disentangle
timbre properties. The emergence of disentangled representations was studied in
Variational Auto-Encoders (VAEs), and has been applied to audio. Using an
additional perceptual regularization can align such latent representation with
the previously established multi-dimensional timbre spaces, while allowing
continuous inference and synthesis. Alternatively, some specific sound
attributes can be learned as control variables while unsupervised dimensions
account for the remaining features. New possibilities for timbre manipulations
are enabled with generative neural networks, although the exploration and the
creative use of their representations remain little. The following experiments
are led in cooperation with two composers and propose new creative directions
to explore latent sound synthesis of musical timbres, using specifically
designed interfaces (Max/MSP, Pure Data) or mappings for descriptor-based
synthesis.
