Online Dereverberation for Dynamic Scenarios Using a Kalman Filter With an Autoregressive Model

Braun, Sebastian; Habets, Emanuël A. P.

doi:10.1109/lsp.2016.2616888

Cited by 40 publications

(43 citation statements)

References 21 publications

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“…The ML-based estimation proposed in [16] can be used to estimate the variance σ 2 k (n) of the desired signal, which may degrade the performance of the RLS algorithm when the speaker position changes. In this paper, the variance is estimated as…”

Section: Rls-based Mclp Methodsmentioning

confidence: 99%

RLS-Based Adaptive Dereverberation Tracing Abrupt Position Change of Target Speaker

Xiang

Lü

Chen

2018

2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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Adaptive algorithm based on multi-channel linear prediction is an effective dereverberation method balancing well between the attenuation of the long-term reverberation and the dereverberated speech quality. However, the abrupt change of the speech source position, usually caused by the shift of the speakers, forms an obstacle to the adaptive algorithm and makes it difficult to guarantee both the fast convergence speed and the optimal steady-state behavior. In this paper, the RLS-based adaptive multi-channel linear prediction method is investigated and a time-varying forgetting factor based on the relative weighted change of the adaptive filter coefficients is proposed to effectively tracing the abrupt change of the target speaker position. The advantages of the proposed scheme are demonstrated in the simulations and experiments.

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Section: Rls-based Mclp Methodsmentioning

confidence: 99%

RLS-Based Adaptive Dereverberation Tracing Abrupt Position Change of Target Speaker

Xiang

Lü

Chen

2018

2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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“…With (14), we may predict x n| (l) from x n (l − l ), which indeed is the fundamental assumption of MCLP-based dereverberation [5]- [19]. Assumptions (13) and (15) allow for unbiased filter estimation [21] in MCLP-based dereverberation [5]- [19] and GSC-based dereverberation and noise reduction [3], [4], respectively. Hence, all three assumptions (13)- (15) are equally essential in the derivation of the ISCLP Kalman filter, cf.…”

Section: Signal Modelmentioning

confidence: 99%

“…Due to this delay and (13), we have E[u LP (l)s * T (l)] = 0, likewise allowing for unconstrained, recursive estimation of w LP (l). However, with both u SC (l) and 1 In MCLP literature, delays of more than one frame are commonly used [8]- [13], [15], [16], [18], [19] in order to avoid temporal target component leakage due to overlapping windows in the STFT processing, cf. Sec.…”

Section: A Isclp Signal Path Architecturementioning

confidence: 99%

Integrated Sidelobe Cancellation and Linear Prediction Kalman Filter for Joint Multi-Microphone Speech Dereverberation, Interfering Speech Cancellation, and Noise Reduction

Dietzen

Doclo

Moonen

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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In multi-microphone speech enhancement, reverberation as well as additive noise and/or interfering speech are commonly suppressed by deconvolution and spatial filtering, e.g., using multi-channel linear prediction (MCLP) on the one hand and beamforming, e.g., a generalized sidelobe canceler (GSC), on the other hand. In this paper, we consider several reverberant speech components, whereof some are to be dereverberated and others to be canceled, as well as a diffuse (e.g., babble) noise component to be suppressed. In order to perform both deconvolution and spatial filtering, we integrate MCLP and the GSC into a novel architecture referred to as integrated sidelobe cancellation and linear prediction (ISCLP), where the sidelobecancellation (SC) filter and the linear prediction (LP) filter operate in parallel, but on different microphone signal frames. Within ISCLP, we estimate both filters jointly by means of a single Kalman filter. We further propose a spectral Wiener gain post-processor, which is shown to relate to the Kalman filter's posterior state estimate. The presented ISCLP Kalman filter is benchmarked against two state-of-the-art approaches, namely first a pair of alternating Kalman filters respectively performing dereverberation and noise reduction, and second an MCLP+GSC Kalman filter cascade. While the ISCLP Kalman filter is roughly M 2 times less expensive than both reference algorithms, where M denotes the number of microphones, it is shown to perform similarly as compared to the former, and to outperform the latter.

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“…For example, if the window is too large then it is not appropriate to treat the segments to be stationary; if, however, the window is too small, the segments turn out to be too short that the estimates may be unreliable. In the second strategy, the TV coefficients of the model are considered as random processes with certain stochastic model structure [11,12]. The main limitation of this scheme is the possible tracking lag presented in the estimated parameters due to the slow convergence rate, which makes these approaches inaccurate for tracking abrupt changes of the underlying signals [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Multiple Beta Wavelet-Based Locally Regularized Ultraorthogonal Forward Regression Algorithm for Time-Varying System Identification With Applications to EEG

Zhang

Cui

et al. 2020

IEEE Trans. Instrum. Meas.

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Online Dereverberation for Dynamic Scenarios Using a Kalman Filter With an Autoregressive Model

Cited by 40 publications

References 21 publications

RLS-Based Adaptive Dereverberation Tracing Abrupt Position Change of Target Speaker

RLS-Based Adaptive Dereverberation Tracing Abrupt Position Change of Target Speaker

Integrated Sidelobe Cancellation and Linear Prediction Kalman Filter for Joint Multi-Microphone Speech Dereverberation, Interfering Speech Cancellation, and Noise Reduction

A Multiple Beta Wavelet-Based Locally Regularized Ultraorthogonal Forward Regression Algorithm for Time-Varying System Identification With Applications to EEG

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