AtResNet: Residual Atrous CNN with Multi-scale Feature Representation for Low Complexity Acoustic Scene Classification

Acoustic Scene Classification (ASC) aims to categorize real-world audio into one of the predetermined classes that identifies the recording environment of the audio. State-of-the-art ASC algorithms have excellent performance in terms of accuracy due to the emergence of deep learning algorithms. In particular, Convolutional Neural Networks (CNN) have set a new benchmark in ASC due to their promising performance. Despite the emergence of new frameworks, the interest in ASC is growing progressively with a shift of focus from enhancing accuracy to reducing model complexity. In this work, we introduce the AtResNet, a residual atrous CNN for low complexity acoustic scene classification. The AtResNet utilizes dilated convolutions and residual connections to reduce the number of model parameters. To further enhance the performance of AtResNet, we introduce a multi-scale feature representation method called multi-scale mel spectrogram (ms2). To compute the ms2, we evaluate the mel spectrogram on the wavelet subbands of the signal. We assessed AtResNet with ms2 on three benchmark datasets in ASC. The results suggest that our method significantly outperformed the CNN-based techniques in addition to a baseline system based on log mel spectrum for signal representation. AtResNet offers a 28.73% reduction in the model parameters against a baseline CNN. Furthermore, the AtResNet has a model size of 81 KB with post-training quantization of network weights. It makes AtResNet suitable for deployment in context-aware devices.