Title: Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals,
Abstract: Interpretability of deep neural networks is a recently emerging area of
machine learning research targeting a better understanding of how models
perform feature selection and derive their classification decisions. In this
paper, two neural network architectures are trained on spectrogram and raw
waveform data for audio classification tasks on a newly created audio dataset
and layer-wise relevance propagation (LRP), a previously proposed
interpretability method, is applied to investigate the models' feature
selection and decision making. It is demonstrated that the networks are highly
reliant on feature marked as relevant by LRP through systematic manipulation of
the input data. Our results show that by making deep audio classifiers
interpretable, one can analyze and compare the properties and strategies of
different models beyond classification accuracy, which potentially opens up new
ways for model improvements.