Implicit Speaker Separation. DaimlerChrysler Research and Technology. Problem Context. Speech recognition. ‚ text ‘. Speaker separation. drivercodriver. +. Algorithm Architecture. Spatial. Adaption during driver silences. Min Power. Filter. . drivercodriver.
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Implicit Speaker Separation
DaimlerChrysler Research and Technology
Speech recognition
‚text‘
Speaker separation
drivercodriver
+
Spatial
Adaption during driver silences
Min Power
Filter

drivercodriver
x1 (signal ref)
+
y1 = x1 + x2* w2
w2
x2 (noise ref)
w2(n+1) (k) = w2(n) (k) m y1(t)x2(tk)/s2x2
0 <m < 2
x1 (signal ref)
+
y1 = x1 + x2* w2
w2
x2 (noise ref)
x1 (signal ref)
+
y1 = x1 + x2* w2
w2
x2 (noise ref)
x1 (signal ref)
+
y1 = x1 + x2* w2
w2
x2 (noise ref)
m (k) = m0s2x2 /s2y1
0 < m0s2x2 /s2y1 < 2
x1 (signal ref)
+
y1 = x1 + x2* w2
w2
x2 (noise ref)
w2(n+1) (k)= w2(n) (k)– m0y1(t)x2(tk)2/s2y1
If not then NLMS with stepsize m0
w2(n+1) (k) = w2(n) (k) m0y1(t)x2(tk)/s2x2
“Implicit” LMS stability condition
x1 (signal ref)
+
y1 = x1 + x2* w2
w2
x2 (noise ref)
w2(n+1) (k)= w2(n) (k)– m0y1(t)x2(tk)2/s2y1
If not then NLMS with stepsize m0
w2(n+1) (k) = w2(n) (k) m0y1(t)x2(tk)/s2x2
When does it happen ?
Replace the noise reference x2 with the best available reference y2.
No adaption control needed (blind).
High complexity w.r.t. NLMS or ILMS
Dependence measure
w1 and w2 are jointly optimized such that the outputs are independent.
x1
+
y1
w1
w2
ILMS (reminder)
+
y2
w2(n+1) = w2(n) – m y1(t)x2 (tk)/s2y1
x2
w1(n+1) = w1(n) – my2(t) y1 (tk)/s2y1
w2(n+1) = w2(n) – my1(t) y2 (tk)/s2y2
Clean signals
SNR at x1 = 15 dB