Consistent Wiener Filtering for Audio Source Separation

By Jonathan Le Roux, Emmanuel Vincent. 2013

113 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Signal Processing

Citations in the corpus

Speech Denoising with Deep Feature Losses (2018, background): “This issue can be alleviated somewhat, but with increased computational cost and system complexity [12, 13, 14, 15, 16, 17, 18].”
End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction (2018, methodology): “To improve the consistency, one stream of research is focused on iterative methods such as the classic Griffin-Lim algorithm [14], multiple input spectrogram inverse (MISI) [15], ISSIR [16], and consistent Wiener filtering [17], which can recover the clean phase to some extent starting from the mixt”
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, unknown): “In the conventional STFT domain [65], [101], [102], it can be”
Differentiable Consistency Constraints for Improved Deep Speech Enhancement (2018, background): “STFT consistency has been exploited by a number of works [5, 6, 7, 8, 9], but usually in a model-based context that requires iterative algorithms.”
Equalization matching of speech recordings in real-world environments (2016, methodology): “Our best guess is to compute the filter G as the ratio: G(ω) = Ya(ω)/Yb(ω) (8)”
Phase Processing for Single-Channel Speech Enhancement: History and recent advances (2015, background): “X X STFT iSTFT ! N N In contrast to this, in [29] the relationship between STFT coefficients across time and frequency is taken into account, leading to the consistent Wiener filter [29], which modifies both the magnitude and the phase of the noisy observation to obtain the separated speech.”