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Fundamentals of Noise Reduction

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Springer Handbook of Speech Processing

Part of the book series: Springer Handbooks ((SHB))

Abstract

The existence of noise is inevitable. In all applications that are related to voice and speech, from sound recording, telecommunications, and telecollaborations, to human-machine interfaces, the signal of interest that is picked up by a microphone is generally contaminated by noise. As a result, the microphone signal has to be cleaned up with digital signal-processing tools before it is stored, analyzed, transmitted, or played out. The cleaning process, which is often referred to as either noise reduction or speech enhancement, has attracted a considerable amount of research and engineering attention for several decades. Remarkable advances have already been made, and this area is continuing to progress, with the aim of creating processors that can extract the desired speech signal as if there is no noise. This chapter presents a methodical overview of the state of the art of noise-reduction algorithms. Based on their theoretical origin, the algorithms are categorized into three fundamental classes: filtering techniques, spectral restoration, and model-based methods. We outline the basic ideas underlying these approaches, discuss their characteristics, explain their intrinsic relationships, and review their advantages and disadvantages.

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Abbreviations

AR:

autoregressive

CE:

categorical estimation

CRLB:

Cramèr-Rao lower bound

DFT:

discrete Fourier transform

EM:

estimate-maximize

FFT:

fast Fourier transform

FIR:

finite impulse response

HMM:

hidden Markov models

HNM:

harmonic-plus-noise model

IDFT:

inverse DFT

LP:

linear prediction

LPC:

linear predictive coding

MAP:

maximum a posteriori

ML:

maximum-likelihood

MMSE:

minimum mean-square error

MOS:

mean opinion score

MSE:

mean-square error

PSD:

power spectral density

SNR:

signal-to-noise ratio

STFT:

short-time Fourier transform

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Author information

Authors and Affiliations

  1. Alcatel-Lucent, Bell Laboratories, 600 Mountain Ave, 07974, Murray Hill, NJ, USA

    Jingdong Chen Dr.

  2. INRS-EMT, University of Quebec, 800 de la Gauchetiere Ouest, H5A 1K6, Montreal, Quebec, Canada

    Jacob Benesty Prof.

  3. Alcatel-Lucent, Bell Laboratories, 600 Mountain Avenue, 07974, Murray Hill, NJ, USA

    Yiteng (Arden) Huang Dr.

  4. Multimedia Technologies Research Department, Avaya Labs Research, 233 Mt. Airy Road, 07920, Basking Ridge, NJ, USA

    Eric J. Diethorn Dr.

Authors
  1. Jingdong Chen Dr.
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  2. Jacob Benesty Prof.
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  3. Yiteng (Arden) Huang Dr.
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  4. Eric J. Diethorn Dr.
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Corresponding authors

Correspondence to Jingdong Chen Dr. , Jacob Benesty Prof. , Yiteng (Arden) Huang Dr. or Eric J. Diethorn Dr. .

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Editors and Affiliations

  1. INRS-EMT, University of Quebec, 800 de la Gauchetiere Ouest, H5A 1K6, Montreal, Quebec, Canada

    Jacob Benesty Dr.

  2. Avayalabs Research, 233 Mount Airy Road, 07920, Basking Ridge, NJ, USA

    M. Mohan Sondhi Ph.D.

  3. Alcatel-Lucent, Bell Laboratories, 600 Mountain Avenue, 07974, Murray Hill, NJ, USA

    Yiteng Arden Huang Dr.

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Chen, J., Benesty, J., Huang, Y.(., Diethorn, E.J. (2008). Fundamentals of Noise Reduction. In: Benesty, J., Sondhi, M.M., Huang, Y.A. (eds) Springer Handbook of Speech Processing. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49127-9_43

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  • DOI: https://doi.org/10.1007/978-3-540-49127-9_43

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