Do WaveNets Dream of Acoustic Waves?

TL;DR

This paper explores how WaveNet, a deep learning model for speech, internally represents acoustic features, revealing that it explicitly extracts pitch and spectral information through its layers.

Contribution

The study provides the first interpretation of WaveNet’s internal mechanisms, showing it learns meaningful acoustic representations and performs pitch extraction without supervision.

Findings

Higher layer activations correlate with spectral features

WaveNet explicitly performs pitch extraction

Latent representations switch between baseband and wideband components

Abstract

Various sources have reported the WaveNet deep learning architecture being able to generate high-quality speech, but to our knowledge there haven't been studies on the interpretation or visualization of trained WaveNets. This study investigates the possibility that WaveNet understands speech by unsupervisedly learning an acoustically meaningful latent representation of the speech signals in its receptive field; we also attempt to interpret the mechanism by which the feature extraction is performed. Suggested by singular value decomposition and linear regression analysis on the activations and known acoustic features (e.g. F0), the key findings are (1) activations in the higher layers are highly correlated with spectral features; (2) WaveNet explicitly performs pitch extraction despite being trained to directly predict the next audio sample and (3) for the said feature analysis to take place, the latent signal representation is converted back and forth between baseband and wideband components.