vschaos is an open-source Python library for variational neural audio synthesis. This library, based on <a href="https://pytorch.org/">pytorch</a>, allows high-level functions for creating, training, and using many variants of variational auto-encoding.
The orchids software is the first complete system for abstract and temporal computer-assisted orchestration and timbral mixture optimization. It provides a set of algorithms and features to reconstruct any time-evolving target sound with a combination of acoustic instruments, given a set of psychoacoustic criteria. It can help composers to achieve unthinkable timbral colors by providing efficient sets of solutions that best match a sound target.
Sound synthesizers are pervasive in music and they now even entirely define new music genres. However, their complexity and sets of parameters renders them difficult to master. We created an innovative generative probabilistic model that learns an invertible mapping between a continuous auditory latent space of a synthesizer audio capabilities and the space of its parameters. We approach this task using variational auto-encoders and normalizing flows Using this new learning model, we can learn the principal macro-controls of a synthesizer, allowing to travel across its organized manifold of sounds, performing parameter inference from audio to control the synthesizer with our voice, and even address semantic dimension learning where we find how the controls fit to given semantic concepts, all within a single model.
We recently developed the first live orchestral piano (LOP) system, a real-time automatic projective orchestration project. The system provides a way to compose music with a full classical orchestra in real-time by simply playing on a MIDI keyboard. Given piano score, the objective is to be able to automatically generate an orchestration.