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《Journal of Data Acquisition & Processing》 2009-01
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Blind Source Separation Algorithm for Convolutive Mixtures Based on Time-Frequency Analysis

Wang Weihua,Huang Fenggang(College of Computer Science and Technology,Harbin Engineering University,Harbin,150001,China)  
A speech blind separation algorithm for convolutive mixtures based on time-frequency analysis is proposed.Because the distances between sources and sensors are different,amplitude attenuations and time delays can be produced with the source signals transmit.The algorithm uses the short-time Fourier transform to process the sensors signals,and constructs the amplitude attenuation vectors and time delay vectors.Using the speech signal sparsity,mixtures in time-frequency domain can be clustered into several classes according to amplitude attenuation vectors and time delay vectors.The representations of source signals in time-frequency domain are obtained by estimating the mixed coefficients.The algorithm is used for the case of more sources than sensors.The simulation shows that the algorithm can separate convolutive mixtures in undetermined case,and results demonstrate encouraging separation performance of the signals.
【CateGory Index】: TN911.7
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