Exploiting Acoustic Similarity of Propagating Paths for Audio Signal Separation
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Exploiting Acoustic Similarity of Propagating Paths for Audio Signal Separation Bin Yin Faculty of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands Storage Signal Processing Group, Philips Research Laboratories, P.O. Box WY-31, 5656 AA Eindhoven, The Netherlands Email: [email protected]
Piet C. W. Sommen Faculty of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands Email: [email protected]
Peiyu He Faculty of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands University of Sichuan, Chengdu 610064, China Email: [email protected] Received 20 September 2002 and in revised form 26 May 2003 Blind signal separation can easily find its position in audio applications where mutually independent sources need to be separated from their microphone mixtures while both room acoustics and sources are unknown. However, the conventional separation algorithms can hardly be implemented in real time due to the high computational complexity. The computational load is mainly caused by either direct or indirect estimation of thousands of acoustic parameters. Aiming at the complexity reduction, in this paper, the acoustic paths are investigated through an acoustic similarity index (ASI). Then a new mixing model is proposed. With closely spaced microphones (5–10 cm apart), the model relieves the computational load of the separation algorithm by reducing the number and length of the filters to be adjusted. To cope with real situations, a blind audio signal separation algorithm (BLASS) is developed on the proposed model. BLASS only uses the second-order statistics (SOS) and performs efficiently in frequency domain. Keywords and phrases: blind signal separation, acoustic similarity, noncausality.
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INTRODUCTION
In recent years, blind signal separation (BSS) has grasped the attention of lots of researchers because of its numerous attractive applications in speech processing, digital communications, medical science, and so on. BSS, within the framework of independent component analysis (ICA) [1, 2], deals with the problem of separating statistically independent sources only from their observed mixtures while both the mixing process and source signals are unknown. For acoustical applications, it can be used to extract individual audio sources from multiple microphone signals when several sources are simultaneously active [3]. In other words, it becomes possible, for instance, in a teleconferencing system, to pick up one desired speech signal under a relatively low signal-to-noise ratio (SNR) (so called “cocktail party effect”). For a certain combination of source-sensor positions, instead of solving three-dimensional wave equations, the
acoustic transmission from the source to the sensor can be simply described using an impulse response, which is obtained by measuring the signal received by the sensor after a sound pulse has been emitted from the source. An exampl
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