Audio signal segmentation is a key task for automatic audio indexing. It
consists of detecting the boundaries of class-homogeneous segments in the
signal. In many applications, explainable AI is a vital process for
transparency of decision-making with machine learning. In this paper, we
propose an explainable multilabel segmentation model that solves speech
activity (SAD), music (MD), noise (ND), and overlapped speech detection (OSD)
simultaneously. This proxy uses the non-negative matrix factorization (NMF) to
map the embedding used for the segmentation to the frequency domain.
Experiments conducted on two datasets show similar performances as the
pre-trained black box model while showing strong explainability features.
Specifically, the frequency bins used for the decision can be easily identified
at both the segment level (local explanations) and global level (class
prototypes).

本文提出了一种可解释的多标签分割模型，它可以同时解决语音活动、音乐、噪声和重叠说话检测的问题。该模型利用非负矩阵分解将用于分割的嵌入映射到频域，具有良好的解释性特征。实验结果表明，该模型在两个数据集上的性能与预训练的黑盒模型相似，同时能够在段落级别和整体级别上轻松识别用于决策的频率区间。