Acoustic scene classification: A comprehensive survey
Acoustic scene classification: A comprehensive survey
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DOI：
10.1016/j.eswa.2023.121902
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发表时间：
2023-10-07
影响因子：
8.5
通讯作者：
Guo, Difei
中科院分区：
文献类型：
--
作者：
Ding, Biyun;Zhang, Tao;Guo, Difei
声学场景分类（ASC）由于其广泛的应用，近年来引起了人们的极大兴趣。各种音频信号处理和机器学习方法已被提出用于ASC。涵盖理论、算法和应用的ASC出版物的数量和范围也得到了扩展。然而，目前还没有全面的调查来收集和组织这些知识，阻碍了研究人员的能力及其应用。为了填补这一空白，我们提出了ASC方法的最新概述，涵盖了早期的工作和最近的进展。在这项工作中，我们首先定义了ASC的一般框架，首先回顾了ASC领域以前的研究。然后，我们回顾了已经取得良好表现的ASC核心技术。聚焦于基于机器学习的ASC系统，本工作总结并分组了数据处理、特征获取和建模方面的现有技术。此外，我们总结了ASC研究的可用资源，并分析了ASC在声学场景和事件的检测和分类（DCASE）挑战中的任务。最后，我们讨论了当前ASC算法的局限性，以及未来ASC系统实际应用可能面临的挑战。
Acoustic scene classification (ASC) has gained significant interest recently due to its diverse applications. Various audio signal processing and machine learning methods have been proposed for ASC. The volume and scope of ASC publications covering theories, algorithms, and applications have also been expanded. However, no recent comprehensive surveys exist to collect and organize the knowledge, impeding the ability of researchers and its applications. To fill this gap, we present an up-to-date overview of ASC methods, covering earlier works and recent advances. In this work, we first define a general framework for ASC, starting with a historical review of previous research in the ASC field. Then, we review core techniques for ASC that have achieved good performance. Focus on machine learning based ASC systems, this work summarizes and groups the existing techniques in terms of data processing, feature acquisition, and modeling. Furthermore, we provide a summary of the available resources for ASC research and analyze ASC tasks in Detection and Classification of Acoustic Scenes and Events (DCASE) challenges. Finally, we discuss limitations of the current ASC algorithms and open challenges to possible future developments toward practical applications of ASC systems.