Audio Segmentation Techniques and Applications Based on Deep Learning

作     者：Aggarwal, Shruti Vasukidevi, G. Selvakanmani, S. Pant, Bhaskar Kaur, Kiranjeet Verma, Amit Binegde, Geleta Negasa 

作者机构：Department of Computer Science and Engineering Thapar University Punjab Patiala147004 India Department of Science and Humanities R.M.K. College of Engineering and Technology R.S.M. Nagar Tamil Nadu Puduvoyal India Department of Artificial Intelligence and Data Science Velammal Institute of Technology Velammal Knowledge Park Tamil Nadu Chennai India Department of Computer Science and Engineering Graphic Era Deemed to be University Bell Road Clement Town Uttarakhand Dehradun248002 India University Center for Research and Development Chandigarh University Punjab Ajitgarh India Department of Computer Science College of Engineering and Technology Mettu University Metu Ethiopia 

出 版 物：《Scientific Programming》 (Sci. Program)

年 卷 期：2022年第2022卷第0期

核心收录：

学科分类：0303[法学-社会学] 0710[理学-生物学] 0810[工学-信息与通信工程] 08[工学] 070206[理学-声学] 0836[工学-生物工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：)e authors thank )apar University  Punjab  for the technical assistance. )e authors appreciate the support from Mettu University  Ethiopia 

主　　题：Speech recognition 

摘      要：Audio processing has become an inseparable part of modern applications in domains ranging from health care to speech-controlled devices. In automated audio segmentation, deep learning plays a vital role. In this article, we are discussing audio segmentation based on deep learning. Audio segmentation divides the digital audio signal into a sequence of segments or frames and then classifies these into various classes such as speech recognition, music, or noise. Segmentation plays an important role in audio signal processing. The most important aspect is to secure a large amount of high-quality data when training a deep learning network. In this study, various application areas, citation records, documents published year-wise, and source-wise analysis are computed using Scopus and Web of Science (WoS) databases. The analysis presented in this paper supports and establishes the significance of the deep learning techniques in audio segmentation. Copyright © 2022 Shruti Aggarwal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.