This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. This is extended to present an inherently interpretable model with high performance. To this end, we propose a novel interpreter design that incorporates non-negative matrix factorization (NMF). In particular, an interpreter is trained to generate a regularized intermediate embedding from hidden layers of a target network, learnt as time-activations of a pre-learnt NMF dictionary. Our methodology allows us to generate intuitive audio-based interpretations that explicitly enhance parts of the input signal most relevant for a network's decision. We demonstrate our method's applicability on a variety of classification tasks, including multi-label data for real-world audio and music.

翻译：本文针对音频处理网络可解释性中的两大核心问题——事后解释与设计性解释展开研究。对于事后解释，我们旨在通过高层音频对象来解释网络决策，这些对象同时可供终端用户收听。该方法进一步扩展为构建一种兼具高性能与内在可解释性的模型。为此，我们提出一种融合非负矩阵分解的新型解释器设计：该解释器被训练为从目标网络的隐藏层生成规则化的中间嵌入表示，该表示被学习为预训练非负矩阵分解字典的时间激活函数。我们的方法能够生成基于音频的直观解释，明确突显对网络决策最相关的输入信号部分。通过在多种分类任务（包括真实场景音频与音乐的多标签数据）上的实验，我们验证了该方法的广泛适用性。