Music Generation and Transformation with Moment Matching-Scattering Inverse Networks
Mathieu Andreux, Stéphane Mallat · Zenodo (CERN European Organization for Nuclear Research) · 2018
We introduce a Moment Matching-Scattering Inverse Network (MM-SIN) to generate and transform musical sounds. The MM-SIN generator is similar to a variational autoencoder or an adversarial network. However, the encoder or the discriminator are not learned, but computed with a scattering transform defined from prior information on sparse time-frequency audio properties. The generator is trained by jointly minimizing the reconstruction loss of an inverse problem, and a generation loss which computes a distance over scattering moments. It has a similar causal architecture as a WaveNet and provides a simpler mathematical model related to time-frequency decompositions. Numerical experiments demonstrate that this MMSIN generates new realistic musical signals. It can transform low-level musical attributes such as pitch with a linear transformation in the embedding space of scattering coefficients.