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• W187128757 abstract "In this paper, we propose a novel method for blind source separation (BSS) based on time-frequency sparseness (TF) that can estimate the number of sources and time-frequency masks, even if the spatial aliasing problem exists. Many previous approaches, such as degenerate unmixing estimation technique (DUET) or observation vector clustering (OVC), are limited to microphone arrays of small spatial extent to avoid spatial aliasing. We develop an offline and an online algorithm that can both deal with spatial aliasing by directly comparing observed and model phase differences using a distance metric that incorporates the phase indeterminacy of 2p and considering all frequency bins simultaneously. Separation is achieved using a linear blind beamformer approach, hence musical noise common to binary masking is avoided. Furthermore, the offline algorithm can estimate the number of sources. Both algorithms are evaluated in simulations and real-world scenarios and show good separation performance." @default.
• W187128757 created "2016-06-24" @default.
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• W187128757 date "2010-01-01" @default.
• W187128757 modified "2023-09-25" @default.
• W187128757 title "Blind Source Separation Based on Time-Frequency Sparseness in the Presence of Spatial Aliasing" @default.
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• W187128757 doi "https://doi.org/10.1007/978-3-642-15995-4_1" @default.
• W187128757 hasPublicationYear "2010" @default.
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