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arXiv · 2409.08655

LMAC-TD: Producing Time Domain Explanations for Audio Classifiers

Eleonora Mancini·Francesco Paissan·Mirco Ravanelli·Cem Subakan
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Abstract

Neural networks are typically black-boxes that remain opaque with regards to their decision mechanisms. Several works in the literature have proposed post-hoc explanation methods to alleviate this issue. This paper proposes LMAC-TD, a post-hoc explanation method that trains a decoder to produce explanations directly in the time domain. This methodology builds upon the foundation of L-MAC, Listenable Maps for Audio Classifiers, a method that produces faithful and listenable explanations. We incorporate SepFormer, a popular transformer-based time-domain source separation architecture. We show through a user study that LMAC-TD significantly improves the audio quality of the produced explanations while not sacrificing from faithfulness.

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Eleonora Mancini, Francesco Paissan, Mirco Ravanelli, Cem Subakan. 2024-09-13. LMAC-TD: Producing Time Domain Explanations for Audio Classifiers. https://arxiv.org/abs/2409.08655

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