On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction

By Mehrez Souden, Jacob Benesty, Sofiène Affes. IEEE Transactions on Audio, Speech, and Language Processing, 2010

351 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3884a7f438f889f1c8d6e159d3004fcb244921dc

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Citations in the corpus

Front-end processing for the CHiME-5 dinner party scenario (2018, methodology): “We employed the MVDR beamformer according to [22, 23]:”
Multi-Channel Deep Clustering: Discriminative Spectral and Spatial Embeddings for Speaker-Independent Speech Separation (2018, methodology): “We also performed oracle minimum variance distortionless response (MVDR) beamforming as in [12,34], but results were significantly worse than the MCWF.”
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, neutral): “This relation and the performance of the PMWF are analyzed in [157].”
An Investigation into the Effectiveness of Enhancement in ASR Training and Test for Chime-5 Dinner Party Transcription (2019, methodology): “The class affiliations computed in the E-step of the EM algorithm are employed to estimate spatial covariance matrices of target signals and interferences, from which the coefficients of an Minimum Variance Distortionless Response (MVDR) beamformer are computed [18].”
Improved MVDR Beamforming Using Single-Channel Mask Prediction Networks (2016, methodology): “where G(f) = Φ−1 noise(f)Φnoisy(f) is computed from the M ×M spatial covariance matrices Φnoise(f) of the noise and Φnoisy(f) of the noisy signal, and λ(f) = trace(G(f)) −M [23, 15].”
The STC System for the CHiME-6 Challenge (2020, methodology): “The basic MVDR-beamforming procedure included in the pb chime 1 package uses spectral masks obtained from GSS.”
Multi-Microphone Neural Speech Separation for Far-Field Multi-Talker Speech Recognition (2018, methodology): “By using theMVDR formulation of [18], the beamformer coefficient vector can be obtained as”
LINEAR PREDICTION-BASED DEREVERBERATION WITH ADVANCED SPEECH ENHANCEMENT AND RECOGNITION TECHNOLOGIES FOR THE REVERB CHALLENGE (2014, methodology): “To reduce ambient noise and potential remaining reverberation components, we use a beamformer that employs the spatial correlation of the microphone array signals [6].”
A Study of Learning Based Beamforming Methods for Speech Recognition (2016, methodology): “Given the mask, the MVDR beamforming weights can be determined as follows [20],”