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.

用卷积神经网络构建高效且可解释的端到端音频深度学习模型，通过在三个标准语音情感识别数据集上的实验验证，我们的框架在效率和可解释性方面表现优越，对抗 Mel 频谱特征有高达 7% 的改进；还揭示了前端层在处理复杂的长波形模式方面的高效性和可解释性，通过 PhysioNet 心音数据库的实例进行了论证，为处理原始波形数据构建了可移植的有效模型解决方案。