  Generative models have thrived in computer vision, enabling unprecedented
image processes. Yet the results in audio remain less advanced. Our project
targets real-time sound synthesis from a reduced set of high-level parameters,
including semantic controls that can be adapted to different sound libraries
and specific tags. These generative variables should allow expressive
modulations of target musical qualities and continuously mix into new styles.
To this extent we train AEs on an orchestral database of individual note
samples, along with their intrinsic attributes: note class, timbre domain and
extended playing techniques. We condition the decoder for control over the
rendered note attributes and use latent adversarial training for learning
expressive style parameters that can ultimately be mixed. We evaluate both
generative performances and latent representation. Our ablation study
demonstrates the effectiveness of the musical conditioning mechanisms. The
proposed model generates notes as magnitude spectrograms from any probabilistic
latent code samples, with expressive control of orchestral timbres and playing
styles. Its training data subsets can directly be visualized in the 3D latent
representation. Waveform rendering can be done offline with GLA. In order to
allow real-time interactions, we fine-tune the decoder with a pretrained MCNN
and embed the full waveform generation pipeline in a plugin. Moreover the
encoder could be used to process new input samples, after manipulating their
latent attribute representation, the decoder can generate sample variations as
an audio effect would. Our solution remains rather fast to train, it can
directly be applied to other sound domains, including an user's libraries with
custom sound tags that could be mapped to specific generative controls. As a
result, it fosters creativity and intuitive audio style experimentations.
