  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).
