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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.2 P.140-154

http://doi.org/10.1631/jzus.C1000020


Split vector quantization for sinusoidal amplitude and frequency


Author(s):  Pejman Mowlaee, Abolghasem Sayadian, Hamid Sheikhzadeh

Affiliation(s):  Department of Electronic Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran

Corresponding email(s):   pmb@es.aau.dk, eeas335@aut.ac.ir, hsheikh@aut.ac.ir

Key Words:  Short-time Fourier transform, Split vector quantization, Sinusoidal modeling, Spectral distortion


Pejman Mowlaee, Abolghasem Sayadian, Hamid Sheikhzadeh. Split vector quantization for sinusoidal amplitude and frequency[J]. Journal of Zhejiang University Science C, 2011, 12(2): 140-154.

@article{title="Split vector quantization for sinusoidal amplitude and frequency",
author="Pejman Mowlaee, Abolghasem Sayadian, Hamid Sheikhzadeh",
journal="Journal of Zhejiang University Science C",
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pages="140-154",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000020"
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%A Hamid Sheikhzadeh
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T1 - Split vector quantization for sinusoidal amplitude and frequency
A1 - Pejman Mowlaee
A1 - Abolghasem Sayadian
A1 - Hamid Sheikhzadeh
J0 - Journal of Zhejiang University Science C
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1000020


Abstract: 
In this paper, we suggest applying tree structure on the sinusoidal parameters. The suggested sinusoidal coder is targeted to find the coded sinusoidal parameters obtained by minimizing a likelihood function in a least square (LS) sense. From a rate-distortion standpoint, we address the problem of how to allocate available bits among different frequency bands to code sinusoids at each frame. For further analyzing the quantization behavior of the proposed method, we assess the quantization performance with respect to other methods: the short-time Fourier transform (STFT) based coder commonly used for speech enhancement or separation, and the line spectral frequency (LSF) coder used in speech coding. Through extensive simulations, we show that the proposed quantizer leads to less spectral distortion as well as higher perceived quality for the re-synthesized signals based on the coded parameters in a model-based approach with respect to previous STFT-based methods. The proposed method lowers the complexity, and, due to its tree-structure, leads to a rapid search capability. It provides flexibility for use in many speaker-independent applications by finding the most likely frequency vectors selected from a list of frequency candidates. Therefore, the proposed quantizer can be considered an attractive candidate for model-based speech applications in both speaker-dependent and speaker-independent scenarios.

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Reference

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