Regularization for Partial Multichannel Equalization for Speech Dereverberation

Kodrasi, Ina; Goetze, Stefan; Doclo, Simon

doi:10.1109/tasl.2013.2260743

Cited by 62 publications

(69 citation statements)

References 27 publications

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“…Impulse response reshaping can be combined with crosstalk cancellation in spatial sound reproduction systems in which it is necessary to control the sounds reaching the ears of a listener [184]. Dereverberation is particularly important for speech, because room reverberation reduces speech intelligibility in telephone and teleconferencing systems and complicates automatic speech recognition [185,186].…”

Section: Room Equalizationmentioning

confidence: 99%

All About Audio Equalization: Solutions and Frontiers

Välimäki

Reiss

2016

Applied Sciences

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Audio equalization is a vast and active research area. The extent of research means that one often cannot identify the preferred technique for a particular problem. This review paper bridges those gaps, systemically providing a deep understanding of the problems and approaches in audio equalization, their relative merits and applications. Digital signal processing techniques for modifying the spectral balance in audio signals and applications of these techniques are reviewed, ranging from classic equalizers to emerging designs based on new advances in signal processing and machine learning. Emphasis is placed on putting the range of approaches within a common mathematical and conceptual framework. The application areas discussed herein are diverse, and include well-defined, solvable problems of filter design subject to constraints, as well as newly emerging challenges that touch on problems in semantics, perception and human computer interaction. Case studies are given in order to illustrate key concepts and how they are applied in practice. We also recommend preferred signal processing approaches for important audio equalization problems. Finally, we discuss current challenges and the uncharted frontiers in this field. The source code for methods discussed in this paper is made available at https://code.soundsoftware.ac.uk/projects/allaboutaudioeq.

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Section: Room Equalizationmentioning

confidence: 99%

All About Audio Equalization: Solutions and Frontiers

Välimäki

Reiss

2016

Applied Sciences

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“…In order to mitigate these detrimental effects of reverberation, several single-channel and A well-known complete multi-channel equalization technique aiming at acoustic system inversion is the multiple-input/output inverse theorem (MINT)-based technique [19], which however suffers from drawbacks in practice. Since the available RIRs typically differ from the true RIRs due to fluctuations (e.g., temperature or position variations [27]) or due to the sensitivity of blind system identification (BSI) and supervised system identification (SSI) methods to near-common zeros or interfering noise [28][29][30], MINT generally fails to invert the true RIRs, possibly leading to severe distortions in the output signal [22][23][24]26]. In order to increase the robustness against RIR perturbations, partial multi-channel equalization techniques, such as relaxed multi-channel least-squares (RMCLS) [23] and partial multi-channel equalization based on MINT (PMINT) [24], have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Since the available RIRs typically differ from the true RIRs due to fluctuations (e.g., temperature or position variations [27]) or due to the sensitivity of blind system identification (BSI) and supervised system identification (SSI) methods to near-common zeros or interfering noise [28][29][30], MINT generally fails to invert the true RIRs, possibly leading to severe distortions in the output signal [22][23][24]26]. In order to increase the robustness against RIR perturbations, partial multi-channel equalization techniques, such as relaxed multi-channel least-squares (RMCLS) [23] and partial multi-channel equalization based on MINT (PMINT) [24], have been proposed. Since early reflections tend to improve speech intelligibility [1][2][3] and late reflections are the major cause of speech intelligibility degradation [4][5][6], the objective of partial equalization techniques is to shorten the overall impulse response by suppressing only the late reflections.…”

Section: Introductionmentioning

confidence: 99%

“…While RMCLS imposes no constraints on the remaining early reflections, PMINT has been shown to be more perceptually advantageous since it also aims to control the remaining early reflections. Although partial equalization techniques can be significantly more robust than MINT, their performance still remains rather susceptible to RIR perturbations [23,24,26]. As a result, several methods have been proposed to further increase the robustness against RIR perturbations.…”

Section: Introductionmentioning

confidence: 99%

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Improving the conditioning of the optimization criterion in acoustic multi-channel equalization using shorter reshaping filters

Kodrasi

Doclo

2018

EURASIP J. Adv. Signal Process.

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In acoustic multi-channel equalization techniques, such as complete multi-channel equalization based on the multiple-input/output inverse theorem (MINT), relaxed multi-channel least-squares (RMCLS), and partial multi-channel equalization based on MINT (PMINT), the length of the reshaping filters is generally chosen such that perfect dereverberation can be achieved for perfectly estimated room impulse responses (RIRs). However, since in practice the available RIRs typically differ from the true RIRs, this reshaping filter length may not be optimal. This paper provides a mathematical analysis of the robustness increase of equalization techniques against RIR perturbations when using a shorter reshaping filter length than conventionally used. Based on the condition number of the (weighted) convolution matrix of the RIRs, a mathematical relationship between the reshaping filter length and the robustness against RIR perturbations is established. It is shown that shorter reshaping filters than conventionally used yield a smaller condition number, i.e., a higher robustness against RIR perturbations. In addition, we propose an automatic non-intrusive procedure for determining the reshaping filter length based on the L-curve. Simulation results confirm that using a shorter reshaping filter length than conventionally used yields a significant increase in robustness against RIR perturbations for MINT, RMCLS, and PMINT. Furthermore, it is shown that PMINT using an optimal intrusively determined reshaping filter length outperforms all other considered techniques. Finally, it is shown that the automatic non-intrusively determined reshaping filter length in PMINT yields a similar performance as the optimal intrusively determined reshaping filter length.

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“…Perfect dereverberation can be achieved [2] provided that the estimates of the RIRs are perfect. Algorithms for designing equalizers that are more robust to estimation errors have been proposed in [3] or [4] for example.…”

Section: Introductionmentioning

confidence: 99%

Noise robust blind system identification algorithms based on a Rayleigh quotient cost function

Doclo

Sharma

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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An important prerequisite for acoustic multi-channel equalization for speech dereverberation involves the identification of the acoustic channels between the source and the microphones. Blind System Identification (BSI) algorithms based on cross-relation error minimization are known to misconverge in the presence of noise. Although algorithms have been proposed in the literature to improve robustness to noise, the estimated room impulse responses are usually constrained to have a flat magnitude spectrum. In this paper, noise robust algorithms based on a Rayleigh quotient cost function are proposed. Unlike the traditional algorithms, the estimated impulse responses are not always forced to have unit norm. Experimental results using simulated room impulse responses and several SNRs show that one of the proposed algorithms outperforms competing algorithms in terms of normalized projection misalignment.

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Regularization for Partial Multichannel Equalization for Speech Dereverberation

Cited by 62 publications

References 27 publications

All About Audio Equalization: Solutions and Frontiers

All About Audio Equalization: Solutions and Frontiers

Improving the conditioning of the optimization criterion in acoustic multi-channel equalization using shorter reshaping filters

Noise robust blind system identification algorithms based on a Rayleigh quotient cost function

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