The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2014.050307
PDF

Audio Content Classification Method Research Based on Two-step Strategy

Author 1: Sumei Liang
Author 2: Xinhua Fan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 3, 2014.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Audio content classification is an interesting and significant issue. Audio classification technique has two basic parts: audio feature extraction and classifier. In general the audio content classification method is firstly to identify the original audio into text, then use the identified text to classify. But the text recognition rate is not high, some words that good for classification are identified by mistake causing that the classification effect is not ideal. In order to solve these problems above, this paper proposes a new effective audio classification method based on two-step strategy. In the first step the features are extracted by using the improved mutual information and classified with Naïve Bayes classifier. After classification of the first step, an unreliable area is determined, and samples with features in this area go on to be classified with the second step. In the second step, textual features extracted with CHI statistic method are used to build a text feature space model. Then audio features containing MFCC and frame energy are combined together with the text features to build a new feature vector space model. Finally, the new feature vector space model is classified using Support Vector Machine (SVM) classifier. The experiments show that the two-step strategy classification method for audio classification achieves great classification performance with the accuracy rate of 97.2%.

Keywords: Two-step Strategy; Audio classification; MFCC; Frame energy; Naive Byes; Support vector machine (SVM)

Sumei Liang and Xinhua Fan, “Audio Content Classification Method Research Based on Two-step Strategy” International Journal of Advanced Computer Science and Applications(IJACSA), 5(3), 2014. http://dx.doi.org/10.14569/IJACSA.2014.050307

@article{Liang2014,
title = {Audio Content Classification Method Research Based on Two-step Strategy},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2014.050307},
url = {http://dx.doi.org/10.14569/IJACSA.2014.050307},
year = {2014},
publisher = {The Science and Information Organization},
volume = {5},
number = {3},
author = {Sumei Liang and Xinhua Fan}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org