Tackling Interpretability in Audio Classification Networks with
  Non-negative Matrix Factorization
Jayneel Parekh Sanjeel Parekh Pavlo Mozharovskyi Gaël Richard Florence d'Alché-Buc
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.