Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals

9 Jul 2018Sören BeckerMarcel AckermannSebastian LapuschkinKlaus-Robert MüllerWojciech Samek

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... (read more)

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