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Fractional Sine Transform Based Single Channel Speech Enhancement Technique

  • Prajna Kunche
  • N. Manikanthababu
Chapter
  • 42 Downloads
Part of the SpringerBriefs in Speech Technology book series (BRIEFSSPEECHTECH)

Abstract

In this chapter the effectiveness of using discrete fractional sine transformation (DFrST) based single channel speech enhancement is studied. The DFrST transforms a signal or function into any intermediate domain between time and frequency, according to the rotation of time frequency distribution via angle or order. In this method, the input noisy speech is transformed from spatial domain to spatial frequency domain using DFrST. The enhanced speech is obtained by the application of one dimensional DFrST in a combination of Wiener filter algorithm with two step noise reduction and harmonic regeneration noise reduction techniques. The performance of the DFrST-approach is demonstrated through the simulation on real world noisy speech signals dataset. Experimental results proved that DFrST reduces the noise and improves the quality of speech with improved SNR, Seg-SNR, and SNR-Loss over the conventional Weiner filter and discrete fractional cosine transform (DFrCT) based approaches. In Sects. 5.1 and 5.1.1, the definition, derivation, and properties of DFrST are described. Some of the applications of DFrST are listed in Sect. 5.1.2. The proposed approach for speech signal enhancement using DFrST is presented in Sec. 5.2. Sections 5.3 and 5.4 cite the performance evaluation and experimental result analysis, respectively. In Sect. 5.5, the chapter concludes with observations and conclusions.

Keywords

Single channel speech enhancement Discrete fractional sine transform Wiener filter Harmonic regeneration Properties of DFrST 

References

  1. NOIZEUS: A noisy speech corpus for evaluation of speech enhancement algorithms. (n.d.). Retrieved from https://ecs.utdallas.edu/loizou/speech/noizeus/
  2. Pei, S. C., & Yeh, M. H. (2001). The discrete fractional cosine and sine transforms. IEEE Transactions on Signal Processing, 49(6), 1198–1207.  https://doi.org/10.1109/78.923302.MathSciNetCrossRefzbMATHGoogle Scholar
  3. Salunke, B. A., & Salunke, S. (2016). Analysis of encrypted images using discrete fractional transforms viz. DFrFT, DFrST and DFrCT. In International Conference on Communication and Signal Processing, ICCSP 2016 (pp. 1425–1429).  https://doi.org/10.1109/ICCSP.2016.7754390
  4. Yoshimura, H. (2014). Fingerprint templates generated by the fractional fourier, cosine and sine transforms and their generation conditions. In 2014 World Congress on Internet Security, WorldCIS 2014 (pp. 30–34).  https://doi.org/10.1109/WorldCIS.2014.7028161

Copyright information

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Prajna Kunche
    • 1
  • N. Manikanthababu
    • 1
  1. 1.Indira Gandhi Centre for Atomic ResearchKalpakkamIndia

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