Audio Segmentation Techniques and Applications Based on Deep Learning

Author:

Aggarwal Shruti1ORCID,G Vasukidevi2,Selvakanmani S.3,Pant Bhaskar4,Kaur Kiranjeet5,Verma Amit5,Binegde Geleta Negasa6ORCID

Affiliation:

1. Department of Computer Science and Engineering, Thapar University, Patiala 147004, Punjab, India

2. Department of Science and Humanities, R.M.K. College of Engineering and Technology, R.S.M. Nagar, Puduvoyal, Tamil Nadu, India

3. Department of Artificial Intelligence and Data Science, Velammal Institute of Technology, Velammal Knowledge Park, Chennai, Tamil Nadu, India

4. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Bell Road, Clement Town 248002, Dehradun, Uttarakhand, India

5. University Center for Research and Development, Chandigarh University, Ajitgarh, Punjab, India

6. Department of Computer Science, College of Engineering and Technology, Mettu University, Metu, Ethiopia

Abstract

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.

Funder

Mettu University, Ethiopia

Publisher

Hindawi Limited

Subject

Computer Science Applications,Software

Reference58 articles.

1. An Overview of Automatic Audio Segmentation

2. Classifying heart sounds using peak location for segmentation and feature construction;E. F. Gomes;Workshop Classifying Heart Sounds,2012

3. A two level strategy for audio segmentation

4. A two-phase method for general audio segmentation;J. X. Zhang

5. Applying neural network on the content-based audio classification;X. Shao

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