• Vol 6, No 2 (2015)
  • Electrical, Electronics, and Computer Engineering

Traditional Psychoacoustic Model and Daubechies Wavelets for Enhanced Speech Coder Performance

Sheetal D. Gunjal, Rajeshree D. Raut

Corresponding email: gunjalsheetal@yahoo.com


Published at : 30 Apr 2015
IJtech : 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

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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

Abstract
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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

References

Frank Baumgarte, ( 2002), Improved Audio Coding Using Psychoacoustic Model Based on a Cochlear Filter Bank, IEEE Transactions on Speech and Audio Processing, Volume 10, Number 7.

Ted Painter, Andrew Spanias, (1997), A Review of Algorithms for Perceptual coding of Digital Audio Signal, IEEE Proceedings on Digital Signal Processing,.

Jagadeesh Kanade, Dr. Sivakumar (2014), A Literature Survey on Psychoacoustic Models and Wavelets in Audio Compression, IJARECE, Volume 03, Issue 1.

Samar Krimi, Kai Auni And Noureddine Ellouze, (2007), An Improved Psychoacoustic Model for Audio Coding Based on Wavelet Packet, IEEE Int. Conf. SETIT 2007 Tumisia.

Khalil Abid, Kais Ouni And Noureddine Ellouze, (2010), Audio Compression Codec using a Dynamic Gammachirp Psychoacoustic Model and a DWT Multiresolution Analysis, IJCSE, Volume 02, Number 04, 2010, ISSN: 0975-3397, pp: 1340-1354.

S. China Venkateswaralu, V. Sridhar, A. Subba Rami Reddy and K. Satya Prasad, (2013) Audio Compression using Munich and Cambridge Filters for Audio Coding with Morlet Wavelet, Global Journal of Computer Science and Technology Software and Data Engg. , Volume 13, Number 5, ISSN: 0975-4172.

Othman O. Khalifa, Serina H. Harding, Aisha- Hassan A Hashin, (2005) Compression Using Wavelet Transform, Int. Journal: Signal Processing, Volume 02, Number 05.

Pramila Srinivasan, Leah H. Jamieson, (1999), High Quality Audio Compression Using an Adaptive Wavelet Packet Decomposition and Psychoacoustic Modeling, IEEE Transaction On Signal Processing, Volume XX, Number XX.

Agbinya J.I. Discrete Wavelet Transform Techniques in Speech Processing. IEEE Tencon .

Shao Y and Chang C. H., Bayesian Separation with Scarsity Promotion in Perceptual Wavelet Domain for Speech Enhancement and Hybrid Speech Recognition, IEEE Transaction on System Man and Cybernetics, Part A: System and Humans, volume 41, Number 02, pp: 284-293.

Mourad Talbi,Chafik Barbarnoussi, Cherif Adnane, (2013), Speech Compression Based on Psychoacoustic Model and A General Approach for Filter Bank Design using Optimization, International Arab Conference on Information Technology (ACIT 2013).

Trina Adrian de Perez, Mini li, Hector McAllister, Norman D. Black, (2000), Noise Reduction an Loudness Compression in a Wavelet Modelling of The Auditory System, IEEE Transaction on Signal Processing, Yr: 2000.

Abdul Mawla M. A. Najih, Abdul Rahman Bin Ramli, V.Prakash and Syed A.R., (2003), Speech Compression using Dicrete Wavelet Transform, 4thIEEE International Conference on Telecommunication Technology Proceedings, 2003.

Davis Pan Motorola,(1995), A Tutorial on MPEG/Audio Compression, IEEE Transaction on Signal Processing, 1995.

Sheetal D.Gunjal, Dr. Rajeshee D. Raut, (2012), Advance Source Coding Techniques for Audio/Speech Signal:A Survey,International journal for Computer Technology and Applications, Volume 03, Number 04, ISSN: 2229-6093, Jul-Aug 2012pp: 1335-1342.

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