Enhancement of Residual Echo for Robust Acoustic Echo Cancellation

Wada, Ted S.; Juang, Biing‐Hwang

doi:10.1109/tasl.2011.2159592

Cited by 26 publications

(20 citation statements)

References 33 publications

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“…Since strong near-end interference may corrupt the error signal of the AEC and cause the adaptive filter to diverge, the robust acoustic echo canceler system [14][15][16] is used, where a error recovery nonlinearity (ERN) allows for continuous updating. To reduce the delay of the frequency-domain adaptive filter [17], the multi-delay adaptive filter structure [18] is used.…”

Section: Robust Acoustic Echo Cancelermentioning

confidence: 99%

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Tuning methodology for speech enhancement algorithms using a simulated conversational database and perceptual objective measures

Giacobello¹,

Wung²,

Pichevar³

et al. 2014

2014 4th Joint Workshop on Hands-Free Speech Communication and Microphone Arrays (HSCMA)

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In this paper, we propose a formal methodology for tuning the parameters of a single-microphone speech enhancement system for hands-free devices. The tuning problem is formulated as a largescale nonlinear programming problem that is solved by a genetic algorithm to determine the global solution. A conversational speech database is automatically generated by modeling the interactivity in telephone conversations, and perceptual objective quality measures are used as the optimization criteria for the automated tuning over the generated database. A subjective listening test is then performed by comparing the automatically tuned system based on objective criteria to the system tuned by expert human listeners. Subjective and objective evaluation result shows that the proposed automated tuning methodology greatly improves the enhanced speech quality, potentially saving resources over manual evaluation, speeding up development and deployment time, and guiding the algorithmic design.

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Section: Robust Acoustic Echo Cancelermentioning

confidence: 99%

“…This procedure begins with a randomly chosen population Π p0q in the space of the feasible values rL, Us and it is repeated until a halting criterion is reached after K iterations. The set of parameters p pKq m P Π pKq that maximizes the cost function will be our estimate:p " arg max p pKq m PΠ pKqΔMOS´ŝrn, p pKq m s, yrns¯(16) …”

mentioning

confidence: 99%

Tuning methodology for speech enhancement algorithms using a simulated conversational database and perceptual objective measures

Giacobello¹,

Wung²,

Pichevar³

et al. 2014

2014 4th Joint Workshop on Hands-Free Speech Communication and Microphone Arrays (HSCMA)

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show abstract

“…The RAEC system [9,10] has recently been proposed to allow for robust update of the adaptive filter coefficients even under heavy near-end interference. The RAEC utilizes ERN, which tries to recover the true error signal prior to the adaptive filter update and can be expressed as a nonlinear clipping function [12], i.e.,…”

Section: Robust Acoustic Echo Cancellationmentioning

confidence: 99%

“…The nonlinear clipping function (4) is one of the several nonlinear functions investigated in [10] that gives the best performance, where the residual echo signal b[n] and the near-end signal v[n] are assumed to be Gaussian distributed and Laplace distributed, respectively. Detailed discussion of the convergence behavior with different choices of nonlinearity functions under different signal model assumptions can be found in [10,13,14].…”

Section: Robust Acoustic Echo Cancellationmentioning

confidence: 99%

“…A robust acoustic echo cancellation (RAEC) system [9,10] has recently been proposed to avoid freezing the adaptive filter during double talk. The RAEC utilizes the error recovery nonlinearity (ERN), which "enhances" the filter estimation error prior to the adaptation, and the noise-robust adaptive step-size [11] with blockiterative adaptation that enables the recovery of lost convergence speed due to the aggressive step-size control.…”

Section: Introductionmentioning

confidence: 99%

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Robust acoustic echo cancellation in the short-time fourier transform domain using adaptive crossband filters

Wung¹,

Giacobello²,

Atkins³

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a robust acoustic echo cancellation (AEC) system in the short-time Fourier transform (STFT) domain using adaptive crossband filters. The STFT-domain AEC allows for a simpler system structure compared to the traditional frequency-domain AEC, which normally requires several applications of the discrete Fourier transform (DFT) and the inverse DFT, while the robust AEC (RAEC) allows for continuous and stable filter updates during double talk without freezing the adaptive filter. The RAEC and the STFT-domain AEC have been investigated in the past in separate studies. In this work we propose a novel algorithm that combines the advantages of both approaches for robust update of the adaptive crossband filters even during double talk. Experimental results confirm the benefit of incorporating the robustness constraint for the adaptive crossband filters and show improved performance in terms of the echo reduction and the predicted sound quality.

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Wavelet-Based ICA Using Maximum Likelihood Estimation and Information-Theoretic Measure for Acoustic Echo Cancellation During Double Talk Situation

Kothandaraman

Arulmozhivarman

2015

Circuits Syst Signal Process

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Enhancement of Residual Echo for Robust Acoustic Echo Cancellation

Cited by 26 publications

References 33 publications

Tuning methodology for speech enhancement algorithms using a simulated conversational database and perceptual objective measures

Tuning methodology for speech enhancement algorithms using a simulated conversational database and perceptual objective measures

Robust acoustic echo cancellation in the short-time fourier transform domain using adaptive crossband filters

Wavelet-Based ICA Using Maximum Likelihood Estimation and Information-Theoretic Measure for Acoustic Echo Cancellation During Double Talk Situation

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