Signal enhancement using beamforming and nonstationarity with applications to speech

By Sharon Gannot, David Burshtein, E. Weinstein. IEEE Transactions on Signal Processing, 2001

734 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2344c5782926a22e2a3e69a3a51bc0437e03be97

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In Computational Mechanics

In Signal Processing

Citations in the corpus

Multichannel Eigenspace Beamforming in a Reverberant Noisy Environment With Multiple Interfering Speech Signals (2009, methodology): “Note a profound difference between the proposed method and the algorithms presented in [21] and [33].”
An analysis of environment, microphone and data simulation mismatches in robust speech recognition (2017, neutral): “The lack of robustness of MVDR and other direction-of-arrival based adaptive beamformers on real data has been known for some time in the audio signal processing community (Gannot et al., 2001; Araki et al., 2003).”
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, neutral): “In [265], a soft mask is proposed based on a mapping between the ITD values and the relative contribution of the target source.”
Global and Local Simplex Representations for Multichannel Source Separation (2020, methodology): “…ﬁrst microphone that serves as a reference microphone: Based on the sparsity assumption (2) we get: where is the relative transfer function (RTF) [33], [34] deﬁned as the ratio between the ATF of the m th microphone and the ATF of the reference microphone, both of which are associated with the j…”
On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction (2010, methodology): “Note that the case of spatially diffuse noise was also investigated and the same conclusions regarding the tradeoff between signal distortion and noise reduction were reached.”
Direction of Arrival Based Spatial Covariance Model for Blind Sound Source Separation (2014, neutral): “More recent advances in beamforming are based on adaptively estimating the noise characteristics and designing blocking matrix for the general sidelobe canceller structure [25], [26].”
New insights into the noise reduction Wiener filter (2006, background): “For example, when multiple realizations of the signal can be accessed, beamforming, source separation, or spatio-temporal filtering techniques can be applied to extract the desired speech signal or to attenuate the unwanted noise [8]–[13].”