  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.
