ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Recently, there has been increasing interest in building efficient audio neural networks for on-device scenarios.Most existing approaches are designed to reduce the size of audio neural networks using methods such as model pruning.In this work, we show that instead of reducing model size using complex methods, eliminating the temporal redundancy in the input audio features (e.g., mel-spectrogram) could be an effective approach for efficient audio classification.To do so, we proposed a family of simple pooling front-ends (SimPFs) which use simple non-parametric pooling operations to reduce the redundant information within the mel-spectrogram.We perform extensive experiments on four audio classification tasks to…