New insights into the noise reduction Wiener filter

By Jingdong Chen, Jacob Benesty, Yiteng Huang, Simon Doclo. IEEE Transactions on Audio, Speech, and Language Processing, 2006

661 citations recorded by the source corpus.

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A Supervised Learning Approach to Monaural Segregation of Reverberant Speech (2009, methodology): “Various methods have been proposed for monaural speech enhancement or separation, including spectral subtraction [8], Wiener ﬁltering [13,33], minimum mean square error (MMSE) estimation [18], and subspace analysis [19].”
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation (2020, neutral): “SDI [31] 2006 It provides a rough distortion [99], [100] measure”
On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction (2010, background): “For completeness, we focus on the performance of the PMWF with respect to the operating conditions, namely, the input signal-to-noise ratio (SNR), the reverberation, the type of noise (spatially coherent or incoherent), and the number of microphones.”
On the Importance of the Pearson Correlation Coefficient in Noise Reduction (2008, methodology): “In other words, the speech-distortionless filter will not distort the clean speech signal but will not improve the output SNR either.”
Exploring Monaural Features for Classification-Based Speech Segregation (2013, methodology): “For example, spectral subtraction [4] and Weiner filtering [6] are two representative techniques.”