Toward end-to-end interpretable convolutional neural networks for waveform signals

• URL: http://arxiv.org/abs/2405.01815v1
• Date: Fri, 3 May 2024 02:24:27 GMT
• Title: Toward end-to-end interpretable convolutional neural networks for waveform signals
• Authors: Linh Vu, Thu Tran, Wern-Han Lim, Raphael Phan,
• Abstract summary: This paper introduces a novel convolutional neural networks (CNN) framework tailored for end-to-end audio deep learning models. 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.
• Score: 0.7499722271664147
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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