• International Journal of Technology (IJTech)
  • Vol 6, No 2 (2015)

Traditional Psychoacoustic Model and Daubechies Wavelets for Enhanced Speech Coder Performance

Traditional Psychoacoustic Model and Daubechies Wavelets for Enhanced Speech Coder Performance

Title: Traditional Psychoacoustic Model and Daubechies Wavelets for Enhanced Speech Coder Performance
Sheetal D. Gunjal, Rajeshree D. Raut

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Published at : 30 Apr 2015
Volume : IJtech Vol 6, No 2 (2015)
DOI : https://doi.org/10.14716/ijtech.v6i2.761

Cite this article as:

Gunjal, S.D., Raut, R.D., 2015. Traditional Psychoacoustic Model and Daubechies Wavelets for Enhanced Speech Coder Performance. International Journal of Technology. Volume 6(2), pp. 190-197

Sheetal D. Gunjal Department of Electronics Engineering, Amrutvahini College of Engineering, Pune Road, Near Pune Nashik Highway, Sangamner, Maharashtra 422608, India
Rajeshree D. Raut Department of Electronics Engineering, Ramdeobaba College of Engineering, Katol Rd, Nagpur, Maharashtra 440013, India
Email to Corresponding Author

Traditional Psychoacoustic Model and Daubechies Wavelets for Enhanced Speech Coder Performance

Speech compression techniques based on traditional psychoacoustic model have been proposed by many researchers. We have suggested Discrete Wavelet Transform (DWT) supported by the same psychoacoustic model for speech compression. This paper presents a traditional psychoacoustic model to process equal partitions of total bandwidth spectrum of audio signal frequency to reduce redundancy by filtering out the tones and noise masker in speech signal. Here, the uniform filter banks are used for efficient computations and selection of appropriate threshold level for better compression of Discrete Wavelet Transformed coefficients. Daubechies wavelet filter bank is a nonlinear and asymmetric wavelet filter bank. It is equivalent to cochlear filter of human hearing system. The resemblance between Daubechies Filter Bank and our hearing system is used to develop the novel speech coder. Results have shown better performance in terms of compression factor (CF) and Signal-to-Noise Ratio (SNR) as compare to the methods suggested earlier.

Psychoacoustic model, Daubechies Wavelet, Discrete Wavelet Transform, Thresholding


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