Focal Modulation Networks for Interpretable Sound Classification
Luca Della Libera Cem Subakan Mirco Ravanelli
The increasing success of deep neural networks has raised concerns about
their inherent black-box nature, posing challenges related to interpretability
and trust. While there has been extensive exploration of interpretation
techniques in vision and language, interpretability in the audio domain has
received limited attention, primarily focusing on post-hoc explanations. This
paper addresses the problem of interpretability by-design in the audio domain
by utilizing the recently proposed attention-free focal modulation networks
(FocalNets). We apply FocalNets to the task of environmental sound
classification for the first time and evaluate their interpretability
properties on the popular ESC-50 dataset. Our method outperforms a similarly
sized vision transformer both in terms of accuracy and interpretability.
Furthermore, it is competitive against PIQ, a method specifically designed for
post-hoc interpretation in the audio domain.