Optimized Speech Dereverberation From Probabilistic Perspective for Time Varying Acoustic Transfer Function

Abstract

A dereverberation technique has been developed that optimally combines multichannel inverse filtering (MIF), beamforming (BF), and non-linear reverberation suppression (NRS). It is robust against acoustic transfer function (ATF) fluctuations and creates less distortion than the NRS alone. The three components are optimally combined from a probabilistic perspective using a unified likelihood function incorporating two probabilistic models. A multichannel probabilistic source model based on a recently proposed local Gaussian model (LGM) provides robustness against ATF fluctuations of the early reflection. A probabilistic reverberant transfer function model (PRTFM) provides robustness against ATF fluctuations of the late reverberation. The MIF and multichannel under-determined source separation (MUSS) are optimized in an iterative manner. The MIF is designed to reduce the time-invariant part of the late reverberation by using optimal time-weighting with reference to the PRTFM and the LGM. The MUSS separates the dereverberated speech signal and the residual reverberation after the MIF, which can be interpreted as an optimized combination of the BF and the NRS. The parameters of the PRTFM and the LGM are optimized based on the MUSS output. Experimental results show that the proposed method is robust against the ATF fluctuations under both single and multiple source conditions.

Spatial correlation model based observation vector clustering and MVDR beamforming for meeting recognition

“…We recently found that steering vector estimation by clustering the time-frequency components of the observation vectors performs well as regards noise reduction of real-world recordings [11,12]. The clustering is performed by taking a cue from the spatial correlation matrix of each speaker location [13][14][15], which is realized by modeling the timefrequency components of the microphone observation vectors with a complex GMM (CGMM). This paper describes the application of our above-mentioned MVDR beamforming scheme to real recordings of multi-speaker meeting conversations.…”

A multichannel diffuse power estimator for dereverberation in the presence of multiple sources

“…Algorithms of the second class are proposed, e. g., in [6][7][8][9], where the acoustic system was described using an auto-regressive model. The approach proposed in [6] estimates the clean speech for a single source based on multichannel linear prediction by enhancing the linear prediction residual of the clean speech.…”

“…The approach proposed in [6] estimates the clean speech for a single source based on multichannel linear prediction by enhancing the linear prediction residual of the clean speech. In [7][8][9], the received signal is expressed using an autoregressive model and the regression coefficients are estimated from the observations. The clean speech is then estimated using the regression coefficients.…”

“…The clean speech is then estimated using the regression coefficients. While in [8,9] multi-source models were employed, the algorithm in [8] is evaluated only for a single-talk scenario. Linear prediction-based dereverberation algorithms are typically computationally complex and sensitive to noise.…”

“…Linear prediction-based dereverberation algorithms are typically computationally complex and sensitive to noise. It is, for example, shown in [9] that the complexity and convergence time greatly increases with the number of sources.…”