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Multimedia Tools and Applications

, Volume 78, Issue 11, pp 15141–15168 | Cite as

Speech and music classification using spectrogram based statistical descriptors and extreme learning machine

  • Gajanan K. BirajdarEmail author
  • Mukesh D. Patil
Article

Abstract

This article proposes a novel feature extraction approach for speech/music classification based on generalized Gaussian distribution descriptors extracted from IIR-CQT spectrogram representation. IIR-CQT spectrogram visual representation provides superior temporal resolution at high frequencies and better spectral resolution for low frequencies compared to the conventional short-time Fourier transform analysis which provides uniform frequency resolution. Multi-level decomposition of the spectrogram image is then performed using the Nonsubsampled Contourlet Transform (NSCT) which a fully shift-invariant, multi-scale, and multi-direction expansion that can preserve the edges of the textural pattern of speech and music. The generalized Gaussian distribution (GGD) parameters are produced using maximum likelihood estimation (MLE) from the NSCT subbands to create the image feature descriptor. Chaos crow search algorithm is employed to chose the most relevant feature sub-set and to discard redundant features and finally the extreme learning machine classifier categorizes input audio segment into speech/music. The experimental results show that the proposed feature descriptor is effective and performs better compared to the existing approaches in the speech/music classification. In addition, mismatched training and testing results are also presented.

Keywords

IIR-CQT spectrogram Nonsubsampled contourlet transform Generalized Gaussian distribution Chaos crow search algorithm ELM classifier 

Notes

Acknowledgments

The authors would like to thank Professor Dan Ellis for providing the Scheirer & Slaney database.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics EngineeringRamrao Adik Institute of TechnologyNavi MumbaiIndia
  2. 2.Department of Electronics & Telecommunication EngineeringRamrao Adik Institute of TechnologyNavi MumbaiIndia

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