Focal Modulation Networks for Interpretable Sound Classification

Autor:	Della Libera, Luca, Subakan, Cem, Ravanelli, Mirco
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to ICASSP 2024 XAI-SA Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.02754 Zobrazit plný text záznamu View this record from Arxiv