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  • Research Article
  • Open Access

Online Speech/Music Segmentation Based on the Variance Mean of Filter Bank Energy

EURASIP Journal on Advances in Signal Processing20092009:628570

  • Received: 6 March 2009
  • Accepted: 2 September 2009
  • Published:


This paper presents a novel feature for online speech/music segmentation based on the variance mean of filter bank energy (VMFBE). The idea that encouraged the feature's construction is energy variation in a narrow frequency sub-band. The energy varies more rapidly, and to a greater extent for speech than for music. Therefore, an energy variance in such a sub-band is greater for speech than for music. The radio broadcast database and the BNSI broadcast news database were used for feature discrimination and segmentation ability evaluation. The calculation procedure of the VMFBE feature has 4 out of 6 steps in common with the MFCC feature calculation procedure. Therefore, it is a very convenient speech/music discriminator for use in real-time automatic speech recognition systems based on MFCC features, because valuable processing time can be saved, and computation load is only slightly increased. Analysis of the feature's speech/music discriminative ability shows an average error rate below 10% for radio broadcast material and it outperforms other features used for comparison, by more than 8%. The proposed feature as a stand-alone speech/music discriminator in a segmentation system achieves an overall accuracy of over 94% on radio broadcast material.


  • Speech Recognition
  • Calculation Procedure
  • Filter Bank
  • Discriminative Ability
  • Automatic Speech Recognition

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Authors’ Affiliations

Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia


© Marko Kos et al. 2009

This article is published under license to BioMed Central Ltd. 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.