Neural Architecture Search on Acoustic Scene Classification

• URL: http://arxiv.org/abs/1912.12825v2
• Date: Wed, 5 Aug 2020 04:58:06 GMT
• Title: Neural Architecture Search on Acoustic Scene Classification
• Authors: Jixiang Li, Chuming Liang, Bo Zhang, Zhao Wang, Fei Xiang, Xiangxiang Chu
• Abstract summary: We propose a lightweight yet high-performing baseline network inspired by MobileNetV2. We explore a dynamic architecture space built on the basis of the proposed baseline. Experimental results demonstrate that our searched network is competent in ASC tasks.
• Score: 13.529070650030313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 lightweight yet high-performing 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 trains a supernet that incorporates all candidate networks and then applies a well-known evolutionary algorithm NSGA-II to discover more efficient networks with higher accuracy and lower computational cost. 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.

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