标题： 声景分类中的神经架构搜索 显示英文标题

标题： Neural Architecture Search on Acoustic Scene Classification

摘要： 卷积神经网络在声景分类（ASC）任务中被广泛采用，但通常计算负担较重。在这项工作中，我们提出了一种轻量级且高性能的基础网络，灵感来源于MobileNetV2，它用单向卷积核替换了方形卷积核，以交替提取时域和频域特征。此外，我们基于所提出的基线网络和最近的神经架构搜索（NAS）范式，探索了一个动态架构空间，首先训练一个包含所有候选网络的超网，然后应用著名的进化算法NSGA-II来发现更高效且具有更高准确性和更低计算成本的网络。实验结果显示，我们的搜索网络在ASC任务中表现出色，在DCASE2018任务5评估集上达到了90.3%的F1分数，标志着新的最先进的性能，同时相比我们的基线网络节省了25%的浮点运算（FLOPs）。 显示英文摘要

摘要： 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.