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Implicit Speaker Separation DaimlerChrysler Research and Technology.

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Presentation on theme: "Implicit Speaker Separation DaimlerChrysler Research and Technology."— Presentation transcript:

1 Implicit Speaker Separation DaimlerChrysler Research and Technology

2 2 Problem Context Speaker separation Speech recognition drivercodriver ‚ text ‘

3 3 Algorithm Architecture Spatial Filte r + - Min Power Adaption during driver silences drivercodriver

4 4 Reminder on Least-Mean Square (LMS) w2w2 x 1 (signal ref) x 2 (noise ref) + The filter w 2 is adapted with the normalized Least-Mean Square (NLMS) algorithm. w 2 (n+1) (k) = w 2 (n) (k) -  y 1 (t)x 2 (t-k) /   x2 Converges if the target is not active: speaker activity detection required Convergence is assured (in the mean) if 0   y 1 = x 1 + x 2 * w 2

5 5 Reminder on Least-Mean Square (LMS) w2w2 x 1 (signal ref) x 2 (noise ref) + y 1 = x 1 + x 2 * w 2 NLMS w 2 (n+1) (k) = w 2 (n) (k) -  y 1 (t)x 2 (t-k) /   x2 Adapts slower when the interferer is loud (for stability)

6 6 From LMS to “Implicit” LMS w2w2 x 1 (signal ref) x 2 (noise ref) + NLMS w 2 (n+1) (k) = w 2 (n) (k) -  y 1 (t)x 2 (t-k) /   x2 Adapts slower when the interferer is loud (for the stability) Implicit LMS w 2 (n+1) (k)= w 2 (n) (k) –    y 1 (t)x 2 (t-k)} /   y1 Adapts slower when the target is loud : less target cancellation Adapts faster when the output (=target) is weak. Adapts… maybe to fast. y 1 = x 1 + x 2 * w 2

7 7 “Implicit” LMS stability condition w2w2 x 1 (signal ref) x 2 (noise ref) + NLMS w 2 (n+1) (k) = w 2 (n) (k) -  y 1 (t)x 2 (t-k) /   x2 ILMS w 2 (n+1) (k)= w 2 (n) (k) –    y 1 (t)x 2 (t-k) 2 /   y1 ILMS = NLMS with time varying step size  k     x2 /   y1 ILMS stability condion: 0 <  k  < 2 0 <     x2 /   y1 < 2 y 1 = x 1 + x 2 * w 2

8 8 “Implicit” LMS stability condition w2w2 x 1 (signal ref) x 2 (noise ref) + ILMS stability condition: 0 <     x2 /   y1 < 2 Fulfilled ? If yes then w 2 (n+1) (k)= w 2 (n) (k) –    y 1 (t)x 2 (t-k) 2 /   y1 If not then NLMS with step-size   w 2 (n+1) (k) = w 2 (n) (k) -    y 1 (t)x 2 (t-k) /   x2 y 1 = x 1 + x 2 * w 2

9 9 “Implicit” LMS stability condition w2w2 x 1 (signal ref) x 2 (noise ref) + ILMS stability condition: 0 <     x2 /   y1 < 2 Fulfilled ? If yes then w 2 (n+1) (k)= w 2 (n) (k) –    y 1 (t)x 2 (t-k) 2 /   y1 If not then NLMS with step-size   w 2 (n+1) (k) = w 2 (n) (k) -    y 1 (t)x 2 (t-k) /   x2 y 1 = x 1 + x 2 * w 2 When does it happen ?

10 10 “Implicit” LMS stability condition We describe the system with  mismatch = “how far is w 2 from optimum”  leakage = “how much driver speech is received in codriver microphone” ILMS stability condition:0 <     x2 /   y1 < 2 is not fulfilled if and only if (i) means: “we are close to optimum” (ii) means: “the driver is weak with respect to codriver” => NLMS normalization is convenient.

11 11 From ILMS to BSS (Blind Source Separation) w1w1 w2w2 y1y1 y2y2 x1x1 x2x2 w 1 and w 2 are jointly optimized such that the outputs are independent. + + Dependence measure Replace the noise reference x 2 with the best available reference y 2. No adaption control needed (blind). High complexity w.r.t. NLMS or ILMS w 2 (n+1) = w 2 (n) –  y 1 (t)x 2 (t-k)/  2 y1 ILMS (reminder) w 2 (n+1) = w 2 (n) –  y 1 (t) y 2 (t-k)/  2 y2 w 1 (n+1) = w 1 (n) –  y 2 (t) y 1 (t-k)/  2 y1

12 12 How does it sound ? Microphone signals: Blocwise adaptation Unsupervised NLMS Supervised NLMS Implicit ILMS BSS Samplewise adaptation Unsupervised NLMS Supervised NLMS Implicit ILMS

13 13 Conclusion NLMS converge fastest (target silent) and… … diverge fastest (double talk). 15 dB SIR improvement with perfect double detection ILMS very robust, no explicit speaker detection dB SIR improvement low compexity BSS robust and converge fast SIR improvement 15 dB high complexity

14 14 SIR Improvement

15 15 Microphone power ratio Clean signalsSNR at x 1 = 15 dB


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