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.

通过使用最近提出的无注意力聚焦调制网络（FocalNets），这篇论文在音频领域首次将 FocalNets 应用于环境声音分类任务，评估了其可解释性在流行的 ESC-50 数据集上的性能。与类似规模的视觉变换器相比，我们的方法在准确性和可解释性方面更加出色，并且在音频领域中针对事后解释的方法 PIQ 具有竞争力。