NDLI: Generalized Wiener filtering with fractional power spectrograms

Content Provider	IEEE Xplore Digital Library
Author	Liutkus, A. Badeau, R.
Copyright Year	2015
Description	Author affiliation: Inst. Mines-Telecom, Telecom ParisTech, Paris, France (Badeau, R.) \|\| Speech Process. Team, Inria, Villers-lès-Nancy, France (Liutkus, A.)
Abstract	In the recent years, many studies have focused on the single-sensor separation of independent waveforms using so-called soft-masking strategies, where the short term Fourier transform of the mixture is multiplied element-wise by a ratio of spectrogram models. When the signals are wide-sense stationary, this strategy is theoretically justified as an optimal Wiener filtering: the power spectrograms of the sources are supposed to add up to yield the power spectrogram of the mixture. However, experience shows that using fractional spectrograms instead, such as the amplitude, yields good performance in practice, because they experimentally better fit the additivity assumption. To the best of our knowledge, no probabilistic interpretation of this filtering procedure was available to date. In this paper, we show that assuming the additivity of fractional spectrograms for the purpose of building soft-masks can be understood as separating locally stationary α-stable harmonizable processes, α-harmonizable in short, thus justifying the procedure theoretically.
Starting Page	266
Ending Page	270
File Size	243867
Page Count	5
File Format	PDF
ISBN	9781467369978
DOI	10.1109/ICASSP.2015.7177973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-19
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Spectrogram Source separation Speech Speech processing Probabilistic logic Time-frequency analysis soft-masks audio source separation probability theory harmonizable processes α-stable random variables
Content Type	Text
Resource Type	Article

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