K. Tatar, D. Bisig, P. Pasquier (2020)
Tatar, K., Bisig, D., & Pasquier, P. (2020). Latent Timbre Synthesis: Audio-based variational auto-encoders for music composition and sound design applications. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05424-2
Overview - 何がすごい?
We present the Latent Timbre Synthesis (LTS), a new audio synthesis method using Deep Learning. The synthesis method allows composers and sound designers to interpolate and extrapolate between the timbre of multiple sounds using the latent space of audio frames. We provide the details of two Variational Autoencoder architectures for LTS, and compare their advantages and drawbacks. The implementation includes a fully working application with graphical user interface, called interpo- late two, which enables practitioners to explore the timbre between two audio excerpts of their selection using interpolation and extrapolation in the latent space of audio frames. Our implementation is open-source, and we aim to improve the accessibility of this technology by providing a guide for users with any technical background.