  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.
