  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.
