TP-MobNet: A Two-pass Mobile Network for Low-complexity Classification of Acoustic Scene

Abstract

Acoustic scene classification (ASC) is a method of recognizing and classifying environments that employ acoustic signals. Various ASC approaches based on deep learning have been developed, with convolutional neural networks (CNNs) proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models. When using ASC systems in the real world, model complexity and device robustness are essential considerations. In this paper, we propose a two-pass mobile network for low-complexity classification of the acoustic scene, named TP-MobNet. With inverse residuals and linear bottlenecks, TP-MobNet is based on MobileNetV2, and following mobile blocks, coordinate attention and two-pass fusion approaches are utilized. The log-range dependencies and precise position information in feature maps can be trained via coordinate attention. By capturing more diverse feature resolutions at the network's end sides, two-pass fusions can also train generalization. Also, the model size is reduced by applying weight quantization to the trained model. By adding weight quantization to the trained model, the model size is also lowered. The TAU Urban Acoustic Scenes 2020 Mobile development set was used for all of the experiments. It has been confirmed that the proposed model, with a model size of 219.6 kB, achieves an accuracy of 73.94%.