Interpretable Convolutional Neural Networks for Efficient End-to-End Processing of Waveform Signals

The proposed IConNet architecture leverages insights from audio signal processing to improve the feature extraction and pattern recognition capabilities of end-to-end deep neural networks for raw waveform signals, while maintaining interpretability.