Time Difference of Arrival Estimation of Speech Source in a Noisy ...

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EE049035 – Spring 2007 Decorrelation Criterion For low SNR we get a poor estimation of Hm( w) from (S1) and (S2), but a good estimation of the noise terms: Φˆ 2 ( ) ˆ b w m Gm( w) = Φˆ 1 ( w) Using initialization u2( w) = Gˆ m( w) , decorrelation becomes a linear set, with the LS solution (LD): b m H −1 H ( ) ˆ ˆ m m 2 m 1 Hm( w) = V V V ⎡ ⎣ Φz z ( w) − u ( w) Φz z ( w) ⎤ ⎦ ; V ≜ Φˆ ( w) − u ( w) Φˆ ( w) z z 2 z z 1 m 1 1 20
EE049035 – Spring 2007 Other Algorithms Also in the paper: Iterative solution that combines decorrelation and the first form of stationarity, using Gauss iterations (GS1) Recursive algorithms based on steepest descent, for each batch algorithm, used for tracking moving speakers. 21
Page 1 and 2: EE049035 - Spring 2007 Time Differe
Page 3 and 4: EE049035 - Spring 2007 Introduction
Page 5 and 6: EE049035 - Spring 2007 Problem Form
Page 7 and 8: EE049035 - Spring 2007 Problem Form
Page 9 and 10: EE049035 - Spring 2007 Acoustic Imp
Page 11 and 12: EE049035 - Spring 2007 Cross Correl
Page 13 and 14: EE049035 - Spring 2007 GCC - Simula
Page 15 and 16: EE049035 - Spring 2007 ATF-s Ratio
Page 17 and 18: EE049035 - Spring 2007 Speech Quasi
Page 19: EE049035 - Spring 2007 Decorrelatio
Page 23 and 24: EE049035 - Spring 2007 GCC S1 S2 LD
Page 25 and 26: EE049035 - Spring 2007 Discussion W
Page 27 and 28: EE049035 - Spring 2007 Analysis Win
Page 29 and 30: EE049035 - Spring 2007 Proposed Ext

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