Neural Architecture Search on Acoustic Scene Classification

Jixiang Li, Chuming Liang, Bo Zhang, Zhao Wang, Fei Xiang, Xiangxiang Chu

Convolutional neural networks are widely adopted in Acoustic Scene Classification (ASC) tasks, but they generally carry a heavy computational burden. In this work, we propose a high-performance yet lightweight baseline network inspired by MobileNetV2, which replaces square convolutional kernels with unidirectional ones to extract features alternately in temporal and frequency dimensions. Furthermore, we explore a dynamic architecture space built on the basis of the proposed baseline with the recent Neural Architecture Search (NAS) paradigm, which first train a supernet that incorporates all candidate architectures and then apply a well-known evolutionary algorithm NSGA-II to discover more efficient networks with higher accuracy and lower computational cost from the supernet. Experimental results demonstrate that our searched network is competent in ASC tasks, which achieves 90.3% F1-score on the DCASE2018 task 5 evaluation set, marking a new state-of-the-art performance while saving 25% of FLOPs compared to our baseline network.

 DOI: 10.21437/Interspeech.2020-0057

Cite as: Li, J., Liang, C., Zhang, B., Wang, Z., Xiang, F., Chu, X. (2020) Neural Architecture Search on Acoustic Scene Classification. Proc. Interspeech 2020, 1171-1175, DOI: 10.21437/Interspeech.2020-0057.

  author={Jixiang Li and Chuming Liang and Bo Zhang and Zhao Wang and Fei Xiang and Xiangxiang Chu},
  title={{Neural Architecture Search on Acoustic Scene Classification}},
  booktitle={Proc. Interspeech 2020},