LMAC-TD: Producing Time Domain Explanations for Audio Classifiers
<|reference_start|>LMAC-TD: Producing Time Domain Explanations for Audio Classifiers: 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.<|reference_end|>
@article{mancini2024lmac-td:,
    title={LMAC-TD: Producing Time Domain Explanations for Audio Classifiers},
    author={Eleonora Mancini, Francesco Paissan, Mirco Ravanelli, Cem Subakan},
    journal={arXiv preprint arXiv:2409.08655},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08655},
    primaryClass={cs.SD cs.AI cs.LG eess.AS eess.SP}
}