Blind Source Separation Finding Needles in Hastacks盲源分离在草堆里找针.pptVIP

Blind Source Separation: Finding Needles in Haystacks Signal Mixtures are Everywhere Cell Phones Radio Astronomy Brain Activity Speech/Music Example: Speech Enhancement Example: Wireless Signal Separation Example: Wireless Signal Separation Example: Wireless Signal Separation Example: Wireless Signal Separation Outline of Talk Blind Source Separation General concepts and approaches Convolutive Blind Source Separation Application to multi-microphone speech recordings Complex Blind Source Separation What differentiates the complex-valued case Conclusions Blind Source Separation (BSS) -A Simple Math Example Let s1(k), s2(k),…, sm(k) be signals of interest Measurements: For 1 ≤ i ≤ m, xi(k) = ai1 s1(k) + ai2 s2(k) + … + aim sm(k) Sensor noise is neglected Dispersion (echo/reverberation) is absent Blind Source Separation Example (continued) Can Show: The si(k)’s can be recovered as yi(k) = bi1 x1(k) + bi2 x2(k) + … + bim xm(k) up to permutation and scaling factors (the matrix B “is like” the inverse of matrix A) Problem: How do you find the demixing bij’s when you don’t know the mixing aij’s or sj(k)’s? Why Blind Source Separation?(Why not Traditional Beamforming?) BSS requires no knowledge of sensor geometry. The system can be uncalibrated, with unmatched sensors. BSS does not need knowledge of source positions relative to the sensor array. BSS requires little to no knowledge of signal types - can push decisions/ detections to the end of the processing chain. What Properties Are Necessary for BSS to Work? Separation can be achieved when (# sensors) ≥ (# of sources) The talker signals {sj(t)} are statistically-independent of each other and are non-Gaussian in amplitude OR have spectra that differ from each other OR are non-stationary Statistical independence is the critical assumption. Entropy is the Key to Source Separation Entropy: A measure of regularity Convolutive Blind Source Separation Mixing system is dispers