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.

翻译：神经网络通常是黑盒模型，其决策机制往往不透明。文献中已有若干工作提出了事后解释方法以缓解此问题。本文提出LMAC-TD，一种在时域直接生成解释的事后解释方法。该方法建立在L-MAC（Listenable Maps for Audio Classifiers）的基础上，后者是一种能够生成忠实且可听化解释的技术。我们引入了SepFormer，一种流行的基于Transformer的时域源分离架构。通过用户研究表明，LMAC-TD在保持解释忠实性的同时，显著提升了生成解释的音频质量。