Multi Switched Split Vector Quantization of Narrowband Speech Signals
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).
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:56681", author = "M. Satya Sai Ram and P. Siddaiah and M. Madhavi Latha", title = "Multi Switched Split Vector Quantization of Narrowband Speech Signals", 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).", volume = "2", number = "1", pages = "102-4", }