A brief overview of speech enhancement with linear filtering

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Abstract

In this paper, we provide an overview of some recently introduced principles and ideas for speech enhancement with linear filtering and explore how these are related and how they can be used in various applications. This is done in a general framework where the speech enhancement problem is stated as a signal vector estimation problem, i.e., with a filter matrix, where the estimate is obtained by means of a matrix-vector product of the filter matrix and the noisy signal vector. In this framework, minimum distortion, minimum variance distortionless response (MVDR), tradeoff, maximum signal-to-noise ratio (SNR), and Wiener filters are derived from the conventional speech enhancement approach and the recently introduced orthogonal decomposition approach. For each of the filters, we derive their properties in terms of output SNR and speech distortion. We then demonstrate how the ideas can be applied to single- and multichannel noise reduction in both the time and frequency domains as well as binaural noise reduction.
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In this paper, we provide an overview of some recently introduced principles and ideas for speech enhancement with linear filtering and explore how these are related and how they can be used in various applications. This is done in a general framework where the speech enhancement problem is stated as a signal vector estimation problem, i.e., with a filter matrix, where the estimate is obtained by means of a matrix-vector product of the filter matrix and the noisy signal vector. In this framework, minimum distortion, minimum variance distortionless response (MVDR), tradeoff, maximum signal-to-noise ratio (SNR), and Wiener filters are derived from the conventional speech enhancement approach and the recently introduced orthogonal decomposition approach. For each of the filters, we derive their properties in terms of output SNR and speech distortion. We then demonstrate how the ideas can be applied to single- and multichannel noise reduction in both the time and frequency domains as well as binaural noise reduction.
Original languageEnglish
Article number1
JournalEurasip Journal on Advances in Signal Processing
Volume2014
Issue number162
Pages (from-to)1-10
Number of pages10
ISSN1687-6172
DOI
StatePublished - Nov 2014
Publication categoryResearch
Peer-reviewedYes

    Research areas

  • Noise reduction, Speech enhancement, Orthogonal decomposition, Performance measures, Optimal linear filtering, Single-channel, Multichannel, Binaural, Time domain, Frequency domain

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