  This paper tackles post-hoc interpretability for audio processing networks.
Our goal is to interpret decisions of a network in terms of high-level audio
objects that are also listenable for the end-user. To this end, we propose a
novel interpreter design that incorporates non-negative matrix factorization
(NMF). In particular, a carefully regularized interpreter module is trained to
take hidden layer representations of the targeted network as input and produce
time activations of pre-learnt NMF components as intermediate outputs. 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 popular benchmarks,
including a real-world multi-label classification task.
