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Generalized Wiener filtering with fractional power spectrograms

Antoine Liutkus 1, 2 Roland Badeau 3
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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 alpha-stable harmonizable processes, alpha-harmonizable in short, thus justifying the procedure theoretically.
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  • HAL Id : hal-01110028, version 2

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Antoine Liutkus, Roland Badeau. Generalized Wiener filtering with fractional power spectrograms. 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2015, Brisbane, Australia. ⟨hal-01110028v2⟩

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