Blind Source eparation Finding Needles in Haystacks：盲源分离在草堆里找针

1、click to edit master text stylessecond levelthird levelfourth levelfifth levelclick to edit master title styleblind source separation: finding needles in haystacksscott c. douglasdepartment of electrical engineeringsouthern methodist usignal mixtures are everywhere cell p

2、hones radio astronomy brain activity speech/musichow do we makesense of it all?example: speech enhancementexample: wireless signal separationexample: wireless signal separationexample: wireless signal separationexample: wireless signal separationoutline of talk blind source separationgeneral concept

3、s and approaches convolutive blind source separationapplication to multi-microphone speech recordings complex blind source separationwhat differentiates the complex-valued case conclusionsblind source separation (bss) -a simple math example let s1(k), s2(k), sm(k) be signals of interest measurements

4、: for 1 i m, xi(k) = ai1 s1(k) + ai2 s2(k) + + aim sm(k) sensor noise is neglected dispersion (echo/reverberation) is absentabs(k)x(k)y(k)blind source separation example (continued)abs(k)x(k)y(k) can show: the si(k)s can be recovered asyi(k) = bi1 x1(k) + bi2 x2(k) + + bim xm(k)up to permutation and

5、 scaling factors (thematrix b “is like” the inverse of matrix a)problem: how do you find the demixing bijswhen you dont know the mixing aijs 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 unm

6、atched 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 (# sens

7、ors) (# of sources) the talker signals sj(t) are statistically-independent of each other and are non-gaussian in amplitude orhave spectra that differ from each other orare non-stationary statistical independence is the critical assumption. entropy is the key to source separationentropy: a measure of

8、 regularity in bss, separated signals are demixed and, have “more order” as a group. first used in 1996 for speech separation. - in physics, entropy increases (less order) - in biology, entropy decreases (more order) convolutive blind source separationgoal of convolutive bss key idea: for convolutiv

9、e bss, sources are arbitrarily filtered and arbitrarily shufflednon-gaussian-based blind source separation basic goal: make the output signals look non-gaussian, because mixtures look “more gaussian” (from the central limit theorem) criteria based on this goal:density modeling contrast functionsprop

10、erty restoral e.g. (non-)constant modulus algorithm implications: separating capability of the criteria will be similarimplementation details (e.g. optimization strategy) will yield performance differencesbss for convolutive mixtures idea: translate separation task into frequency domain and apply mu

11、ltiple independent instantaneous bss proceduresdoes not work due to permutation problems a better idea: reformulate separation tasks in the context of multichannel filteringseparation criterion “stays” in the time domain no implied permutation problemcan still employ fast convolution methods for eff

12、icient implementationnatural gradient convolutive bss alg. amari/douglas/cichocki/yang 1997where f(y) is a simple vector-valued nonlinearity.criterion: density-based (maximum likelihood)complexity: about four multiply/adds per tap =blind source separation toolbox a matlab toolbox of robust source se

13、paration algorithms for noisy convolutive mixtures (developed under govt. contract) allows us to evaluate relationships and tradeoffs between different approaches easily and rapidly used to determine when a particular algorithm or approach is appropriate for a particular (acoustic) measurement scena

14、rio speech enhancement methodsclassic (frequency selective) linear filteringonly useful for the simplest of situationssingle-microphone spectral subtraction:only useful if the signal is reasonably well-separated to begin with ( 5db sinr )tends to introduce “musical” artifactsresearch focus: how to l

15、everage multiple microphones to achieve robust signal enhancement with minimal knowledge. novel techniques for speech enhancementblind source separation: find all the talker signals in the room - loud and soft, high and low-pitched, near and far away without knowledge of any of these characteristics

16、.multi-microphone signal enhancement: using only the knowledge of “target present” or “target absent” labels on the data, pull out the target signal from the noisy background. smu multimedia systems labacoustic facilityroom (nominal configuration)acoustically-treatedrt = 300 msnon-parallel walls to

17、prevent flutter echosourcesloudspeakers playing recordings as well as “live” talkers.distance to mics: 50 cmangles: -30o, 0o, 27.5osensorsomnidirectional micro- phones (at803b)linear array (4cm spacing) data collection and processing entirely within matlab. allows for careful characterization, fast

18、evaluation, and experimentation with artificial and human talkers. performance improvement: between 10 db and 15 db for “equal-level” mixtures, and even higher for unequal-level ones.blind source separation exampleconvolutive mixing (room)separation system (code) talker 1(mg) talker 2(scd)noise sour

19、cenoise sourcespeech sourcelinear processingadaptivealgorithmmulti-microphone speech enhancementcontains most speechcontains most noisey1y2y3ynz1z2z3znspeech enhancement via iterative multichannel filtering system output at time k: a linear adaptive filter is a sequence of (n x n) matrices at iterat

20、ion k. goal: adapt , over time such that the multichannel output contains signals with maximum speech energy in the first output. multichannel speech enhancement algorithma novel* technique for enhancing target speech in noise using two or more microphones via joint decorrelationrequires rough targe

21、t identifier (i.e. when talker speech is present) is adaptive to changing noise characteristicsknowledge of source locations, microphone positions, other characteristics not needed.details in gupta and douglas, ieee trans. audio, speech, lang. proc., may 2009 *patent pending28performance evaluations

22、roomacoustically-treated, rt = 300 msnon-parallel walls to prevent flutter echosourcesloudspeakers playing bbc recordings (fs = 8khz), 1 male/1-2 noise sourcesdistance to mics: 1.3 mangles: -30o, 0o, 27.5osensorslinear array adjustable (4cm spacing)roomordinary conference room (rt=600ms)sourceslouds

23、peakers playing bbc recordings (fs = 8khz), 1 male/1-2 noise sourcesangles: -15o, 15o, 30osensorsomnidirectional microphones (at803b)linear array adjustable (4cm nominal spacing)678678audio examples acoustic lab: initial sir = -10db, 3-mic systembefore:after: acoustic lab: initial sir = 0db, 2-mic s

24、ystembefore:after: conference room: initial sir = -10db, 3-mic systembefore:after: conference room: initial sir = 5db, 2-mic systembefore:after:effect of noise segment length on overall performance31diffuse noise source example noise source: smu campus-wide air handling system data was recorded usin

25、g a simple two-channel portable m-audio recorder (16-bit, 48khz) with it associated “t”-shaped omnidirectional stereo array at arms length, then downsampled to 8khz.32air handler data processing step 1: spatio-temporal gevd processing on a frame-by-frame basis with l = 256, where rv(k) = ry(k-1); th

26、at is, data was whitened to the previous frame. step 2: least-squares multichannel linear prediction was used to remove tones. step 3: log-stsa spectral subtraction was applied to the first output channel. complex blind source separationabs(k)x(k)y(k) signal model: x(k) = a s(k) both the si(k)s in s

27、(k) and the elements of a are complex-valued. separating matrix b is complex-valued as well. it appears that there is little difference from the real-valued casecomplex circular vs. complex non-circular sources (second-order) circular source: the energies of the real and imaginary parts of si(k) are

28、 the same. (second-order) non-circular source: the energies of the real and imaginary parts of si(k) are not the same. non-circularcircularcircularwhy complex circularity matters in blind source separationfact #1: it is possible to separate non-circular sources by decorrelation alone if their non-ci

29、rcularities differ eriksson and koivunen, ieee trans. it, 2006 fact #2: the strong-uncorrelating transform is a unique linear transformation for identifying non-circular source subspaces using only covariance matrices. fact #3: knowledge of source non-circularity is required to obtain the best performance of a complex bss procedure. complex fixed poi