Cascade Multi-Channel Noise Reduction and Acoustic Feedback Cancellation

Ruiz, Santiago; Waterschoot, Toon van; Moonen, Marc

doi:10.1109/icassp43922.2022.9747291

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…In [21], the authors presented two cascade algorithms for combined multi-microphone AFC and NR for speech applications using a PEM-based AFC algorithm and MWF. The aim of these cascade algorithms is to estimate a desired speech signal without the feedback and noise components, as observed at a chosen reference microphone.…”

Section: Introductionmentioning

confidence: 99%

“…A scenario with M microphones and one loudspeaker is considered, without loss of generality. The first algorithm in [21] is the baseline algorithm, namely the cascade M-channel rank-1 MWF and PEM-AFC, where a NR stage is performed first using a rank-1 MWF followed by a single-channel AFC stage using the PEM-based AFC algorithm. It is shown by means of simulations that this algorithm does not improve the added stable gain (ASG) in the closed-loop system.…”

Section: Introductionmentioning

confidence: 99%

“…The two desired signal estimates are later used in a single-channel PEM-based AFC stage [7,22]. Although in cascade algorithms where NR is performed first and then AFC, the estimation of the feedback path is usually affected by the NR stage, it is shown in [21] that by performing a rank-2 approximation of the speech correlation matrix this issue can be avoided and the feedback path can be correctly estimated.…”

Section: Introductionmentioning

confidence: 99%

“…The contributions of this paper in comparison to [21] are as follows. A third cascade algorithm for AFC and NR using the PEM-based AFC algorithm and MWF is presented, and then the three algorithms are further analyzed and compared.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Cascade algorithms for combined acoustic feedback cancelation and noise reduction

Ruiz,

van Waterschoot,

Moonen

2023

J AUDIO SPEECH MUSIC PROC.

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This paper presents three cascade algorithms for combined acoustic feedback cancelation (AFC) and noise reduction (NR) in speech applications. A prediction error method (PEM)-based adaptive feedback cancelation (PEM-based AFC) algorithm is used for the AFC stage, while a multichannel Wiener filter (MWF) is applied for the NR stage. A scenario with M microphones and 1 loudspeaker is considered, without loss of generality. The first algorithm is the baseline algorithm, namely the cascade M-channel rank-1 MWF and PEM-AFC, where a NR stage is performed first using a rank-1 MWF followed by a single-channel AFC stage using a PEM-based AFC algorithm. The second algorithm is the cascade $$(M+1)$$ ( M + 1 ) -channel rank-2 MWF and PEM-AFC, where again a NR stage is applied first followed by a single-channel AFC stage. The novelty of this algorithm is to consider an ($$M+1$$ M + 1 )-channel data model in the MWF formulation with two different desired signals, i.e., the speech component in the reference microphone signal and in the loudspeaker signal, both defined by the speech source signal but not equal to each other. The two desired signal estimates are later used in a single-channel PEM-based AFC stage. The third algorithm is the cascade M-channel PEM-AFC and rank-1 MWF where an M-channel AFC stage is performed first followed by an M-channel NR stage. Although in cascade algorithms where NR is performed first and then AFC the estimation of the feedback path is usually affected by the NR stage, it is shown here that by performing a rank-2 approximation of the speech correlation matrix this issue can be avoided and the feedback path can be correctly estimated. The performance of the algorithms is assessed by means of closed-loop simulations where it is shown that for the considered input signal-to-noise ratios (iSNRs) the cascade $$(M+1)$$ ( M + 1 ) -channel rank-2 MWF and PEM-AFC and the cascade M-channel PEM-AFC and rank-1 MWF algorithms outperform the cascade M-channel rank-1 MWF and PEM-AFC algorithm in terms of the added stable gain (ASG) and misadjustment (Mis) as well as in terms of perceptual metrics such as the short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and signal distortion (SD).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cascade algorithms for combined acoustic feedback cancelation and noise reduction

Ruiz,

van Waterschoot,

Moonen

2023

J AUDIO SPEECH MUSIC PROC.

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Centralized Cascade Multi-Channel Noise Reduction and Acoustic Feedback Cancellation in a Wireless Acoustic Sensor And Actuator Network

Ruiz

Waterschoot

Moonen

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A deep learning solution to the marginal stability problems of acoustic feedback systems for hearing aids

Zheng

Wang

et al. 2022

The Journal of the Acoustical Society of America

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For hearing aids, it is critical to reduce the acoustic coupling between the receiver and microphone to ensure that prescribed gains are below the maximum stable gain, thus preventing acoustic feedback. Methods for doing this include fixed and adaptive feedback cancellation, phase modulation, and gain reduction. However, the behavior of hearing aids in situations where the prescribed gain is only just below the maximum stable gain, called here “marginally stable gain,” is not well understood. This paper analyzed marginally stable systems and identified three problems, including increased gain at frequencies with the smallest gain margin, short whistles caused by the limited rate of decay of the output when the input drops, and coloration effects. A deep learning framework, called deep marginal feedback cancellation (DeepMFC), was developed to suppress short whistles, and reduce coloration effects, as well as to limit excess amplification at certain frequencies. To implement DeepMFC, many receiver signals in closed-loop systems and corresponding open-loop systems were simulated, and the receiver signals of the closed-loop and open-loop systems were paired together to obtain parallel signals for training. DeepMFC achieved much better performance than existing feedback control methods using objective and subjective measures.

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Cascade Multi-Channel Noise Reduction and Acoustic Feedback Cancellation

Cited by 4 publications

References 20 publications

Cascade algorithms for combined acoustic feedback cancelation and noise reduction

Cascade algorithms for combined acoustic feedback cancelation and noise reduction

Centralized Cascade Multi-Channel Noise Reduction and Acoustic Feedback Cancellation in a Wireless Acoustic Sensor And Actuator Network

A deep learning solution to the marginal stability problems of acoustic feedback systems for hearing aids

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