Applications of wavelets to Speech processing: A case study of a celp coder

Wavelets are a new family of orthogonal basis functions for representing finite energy signals. In this chapter, we provide a brief review of wavelets and their properties. We cite a number of applications of wavelets to speech processing that have been proposed recently. As a detailed case study in wavelet applications, we present our work on a wavelet-transform-based CELP coder design for high-quality speech coding at about 4.8 kbits/s. The coder quantizes the second residual using a wavelet transform approach instead of the stochastic-codebook-based vector quantization normally used in CELP coders, including the U.S. Federal Standard FS 1016 coder at 4.8 kbits/s. The wavelet coder improves the computational efficiency for encoding the second residual by requiring only 1.2 MIPS instead of 8.3 MIPS required by FS 1016. Subjective speech quality tests involving pairwise comparisons show that the wavelet coder was preferred 61% of the time over FS 1016.

[1]  Petros Maragos,et al.  AM-FM energy detection and separation in noise using multiband energy operators , 1993, IEEE Trans. Signal Process..

[2]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[3]  James M. Ooi Application of wavelets to speech coding , 1993 .

[4]  T. Barnwell Subband coder design incorporating recursive quadrature filters and optimum ADPCM coders , 1982 .

[5]  Mark J. T. Smith,et al.  Exact reconstruction techniques for tree-structured subband coders , 1986, IEEE Trans. Acoust. Speech Signal Process..

[6]  Deepen Sinha,et al.  Low bit rate transparent audio compression using adapted wavelets , 1993, IEEE Trans. Signal Process..

[7]  Randy K. Young Wavelet theory and its applications , 1993, The Kluwer international series in engineering and computer science.

[8]  Shubha Kadambe,et al.  Application of the wavelet transform for pitch detection of speech signals , 1992, IEEE Trans. Inf. Theory.

[9]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Kuansan Wang,et al.  Auditory representations of acoustic signals , 1992, IEEE Trans. Inf. Theory.

[11]  I. Daubechies Orthonormal bases of compactly supported wavelets , 1988 .

[12]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[13]  Ahmed H. Tewfik,et al.  Fast positive definite linear system solvers , 1994, IEEE Trans. Signal Process..

[14]  Christopher Heil,et al.  Continuous and Discrete Wavelet Transforms , 1989, SIAM Rev..

[15]  Gianpaolo Evangelista,et al.  Pitch-synchronous wavelet representations of speech and music signals , 1993, IEEE Trans. Signal Process..

[16]  Ronald E. Crochiere,et al.  Sub-band coder design incorporating quadrature filters and pitch prediction , 1979, ICASSP.

[17]  Ahmed H. Tewfik,et al.  Real-time implementation of second generation of audio multilevel information coding , 1994, Defense, Security, and Sensing.

[18]  Joseph P. Campbell,et al.  The Dod 4.8 Kbps Standard (Proposed Federal Standard 1016) , 1991 .

[19]  Deepen Sinha,et al.  Low bit rate transparent audio compression using a dynamic dictionary and optimized wavelets , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[20]  Gilbert Strang,et al.  Wavelets and Dilation Equations: A Brief Introduction , 1989, SIAM Rev..