Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals
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. This paper explores the interpretability of neural networks in the audio domain by using the previously proposed technique of layer-wise relevance propagation (LRP)... (read more)
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Methods used in the Paper
| METHOD | TYPE | |
|---|---|---|
Interpretability
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Image Models |