Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30073
Multi Switched Split Vector Quantization of Narrowband Speech Signals

Authors: M. Satya Sai Ram, P. Siddaiah, M. Madhavi Latha

Abstract:

Vector quantization is a powerful tool for speech coding applications. This paper deals with LPC Coding of speech signals which uses a new technique called Multi Switched Split Vector Quantization (MSSVQ), which is a hybrid of Multi, switched, split vector quantization techniques. The spectral distortion performance, computational complexity, and memory requirements of MSSVQ are compared to split vector quantization (SVQ), multi stage vector quantization(MSVQ) and switched split vector quantization (SSVQ) techniques. It has been proved from results that MSSVQ has better spectral distortion performance, lower computational complexity and lower memory requirements when compared to all the above mentioned product code vector quantization techniques. Computational complexity is measured in floating point operations (flops), and memory requirements is measured in (floats).

Keywords: Linear predictive Coding, Multi stage vectorquantization, Switched Split vector quantization, Split vectorquantization, Line Spectral Frequencies (LSF).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334095

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1273

References:


[1] Atal, B.S. The history of linear prediction. IEEE Signal Processing Magazine, Vol 23, pp.154-161, March 2006.
[2] Harma, A. Linear predictive coding with modified filter structures. IEEE Trans. Speech Audio Process, Vol 9, pp.769-777, Nov 2001.
[3] Gray, R.M., Neuhoff, D.L.. Quantization. IEEE Trans. Inform. Theory, pp.2325-2383, 1998.
[4] Stephen, So., & Paliwal, K. K. Efficient product code vector quantization using switched split vector quantiser. Digital Signal Processing journal, Elsevier, Vol 17, pp.138-171, Jan 2007.
[5] Paliwal., K.K, Atal, B.S. Efficient vector quantization of LPC Parameters at 24 bits/frame. IEEE Trans. Speech Audio Process, pp.3- 14,1993.
[6] Sara Grassi., "Optimized Implementation of Speech Processing Algorithms," Electronics and Signal Processing Laboratory, Institute of Micro Technology, University of Neuchatel, Breguet 2, CH- 2000 Neuchatel, Switzerland, 1988.
[7] Bastiaan Kleijn., W. Fellow, IEEE, Tom Backstrom., & Paavo Alku. On Line Spectral Frequencies. IEEE Signal Processing Letters, Vol.10, no.3, 2003.
[8] Soong, F., & Juang, B. Line spectrum pair (LSP) and speech data compression. IEEE International Conference on ICASSP, 9, pp 37- 40,1984.
[9] P. Kabal and P. Rama Chandran. "The Computation of Line Spectral Frequencies Using Chebyshev polynomials" IEEE Trans. On Acoustics, Speech Signal Processing, vol 34, No.6, pp. 1419-1426, 1986.
[10] Linde, Y., Buzo, A., & Gray, R. M. An Algorithm for Vector Quantizer Design. IEEE Trans.Commun, 28, pp. 84-95, Jan. 1980.