计算机科学
预处理器
领域(数学)
人工智能
特征提取
特征(语言学)
数据科学
机器学习
信号(编程语言)
环境噪声
语音识别
声音(地理)
地貌学
地质学
哲学
程序设计语言
纯数学
语言学
数学
作者
Anam Bansal,Naresh Kumar Garg
标识
DOI:10.1016/j.iswa.2022.200115
摘要
Automatic environmental sound classification (ESC) is one of the upcoming areas of research as most of the traditional studies are focused on speech and music signals. Classifying environmental sounds such as glass breaking, helicopter, baby crying and many more can aid in surveillance systems as well as criminal investigations. In this paper, a vast range of literature in the field of ESC is elucidated from various facets like preprocessing, feature extraction, and classification techniques. Researchers have used various noise removal and signal enhancement techniques to preprocess the signals. This paper explicates multitude of datasets used in recent studies along with the year of publication and maximum accuracy achieved with the dataset. Deep Neural Networks surpass the traditional machine learning classifiers. The future challenges and prospective research in this field are proposed. Since no recent review on ESC has been published, this study will open up novel ways for certain business applications and security systems.
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