Non-Stationary Noise PSD Matrix Estimation for Multichannel Blind Speech Extraction

Taseska, Maja; Habets, Emanuël A. P.

doi:10.1109/taslp.2017.2750239

Cited by 15 publications

(39 citation statements)

References 27 publications

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“…A major drawback of the MCRA scheme is the lack of robustness in case of a time-varying SNR, which makes noise changes to be detected as speech presence. More recently, the authors of [5] proposed to use spatial information in the SAP estimation, specifically the coherent-to-diffuse ratio (CDR). Alternatively, the power level difference (PLD) between microphones is used in Reference [17] to update the noise statistics, as it is a good indicator of speech presence in CT conditions.…”

Section: A Priori Sap Estimationmentioning

confidence: 99%

“…First, an SCMΦ Y (t, f ) is estimated using a rectangular window of eight past frames as in Reference [5]. Then, we calculate (1) the power spectral density (PSD) ratio between microphones as…”

Section: A Priori Sap Estimationmentioning

confidence: 99%

“…with w = 10 (the window width is 2w + 1). Then, the local a priori SAP estimate is obtained as in Reference [5] using Γ Y 12 (t, f ),…”

Section: A Priori Sap Estimationmentioning

confidence: 99%

“…The performance of MVDR depends on an accurate estimation of the noise spatial characteristics and the acoustic transfer function (ATF) between the target speaker and the microphones. For the estimation of the noise spatial statistics, methods based on multi-channel speech presence probability (SPP) [4,5] are commonly used when there is no prior knowledge of the signal propagation or the spatial structure of the noise. These techniques update the noise information when speech absence is detected, which allows for the tracking of time-varying noise signals.…”

Section: Introductionmentioning

confidence: 99%

“…The estimation of the a posteriori SPP can be addressed assuming complex multivariate Gaussian distributions for the noisy speech Y [4,5], according to the two hypotheses previously formulated (see (36) and (37)). Using the Bayes' rule, the a posteriori SPP at each time-frequency bin can be calculated as…”

mentioning

confidence: 99%

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Dual-Channel Speech Enhancement Based on Extended Kalman Filter Relative Transfer Function Estimation

et al. 2019

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This paper deals with speech enhancement in dual-microphone smartphones using beamforming along with postfiltering techniques. The performance of these algorithms relies on a good estimation of the acoustic channel and speech and noise statistics. In this work we present a speech enhancement system that combines the estimation of the relative transfer function (RTF) between microphones using an extended Kalman filter framework with a novel speech presence probability estimator intended to track the noise statistics' variability. The available dual-channel information is exploited to obtain more reliable estimates of clean speech statistics. Noise reduction is further improved by means of postfiltering techniques that take advantage of the speech presence estimation. Our proposal is evaluated in different reverberant and noisy environments when the smartphone is used in both close-talk and far-talk positions. The experimental results show that our system achieves improvements in terms of noise reduction, low speech distortion and better speech intelligibility compared to other state-of-the-art approaches.

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Section: A Priori Sap Estimationmentioning

confidence: 99%

Section: A Priori Sap Estimationmentioning

confidence: 99%

“…with w = 10 (the window width is 2w + 1). Then, the local a priori SAP estimate is obtained as in Reference [5] using Γ Y 12 (t, f ),…”

Section: A Priori Sap Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%