Toward end-to-end interpretable convolutional neural networks for waveform signals
<|reference_start|>Toward end-to-end interpretable convolutional neural networks for waveform signals: This paper introduces a novel convolutional neural networks (CNN) framework tailored for end-to-end audio deep learning models, presenting advancements in efficiency and explainability. By benchmarking experiments on three standard speech emotion recognition datasets with five-fold cross-validation, our framework outperforms Mel spectrogram features by up to seven percent. It can potentially replace the Mel-Frequency Cepstral Coefficients (MFCC) while remaining lightweight. Furthermore, we demonstrate the efficiency and interpretability of the front-end layer using the PhysioNet Heart Sound Database, illustrating its ability to handle and capture intricate long waveform patterns. Our contributions offer a portable solution for building efficient and interpretable models for raw waveform data.<|reference_end|>
@article{vu2024toward,
    title={Toward end-to-end interpretable convolutional neural networks for
  waveform signals},
    author={Linh Vu, Thu Tran, Wern-Han Lim, Raphael Phan},
    journal={arXiv preprint arXiv:2405.01815},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01815},
    primaryClass={cs.SD cs.AI eess.AS}
}