Nonlinear Blind Source Separation Using Hybrid Neural Networks*

Transcription

1 Nolear Bld Source Separato Usg Hybrd Neural Networks* Chu-Hou Zheg,2, Zh-Ka Huag,2, chael R. Lyu 3, ad Tat-g Lok 4 Itellget Computg Lab, Isttute of Itellget aches, Chese Academy of Sceces, P.O.Box 3, Hefe, Ahu, Cha 2 Departmet of Automato, Uversty of Scece ad Techology of Cha 3 Computer Scece & Egeerg Dept., The Chese Uversty of Hog Kog, Hog Kog 4 Iformato Egeerg Dept., The Chese Uversty of Hog Kog, Shat, Hog Kog zhegch@m.ac.c Abstract. Ths paper proposes a ovel algorthm based o mmzg mutual formato for a specal case of olear bld source separato: postolear bld source separato. A etwork composed of a set of radal bass fucto (RBF) etworks, a set of multlayer perceptro ad a lear etwork s used as a demxg system to separate sources post-olear mxtures. The expermetal results show that our proposed method s effectve, ad they also show that the local character of the RBF etwork s uts allows a sgfcat speedup the trag of the system. Itroducto Bld source separato (BSS) stataeous ad covolute lear mxture has bee tesvely studed over the last decade. ost of the bld separato algorthms are based o the theory of the depedet compoet aalyss (ICA) whe the mxture model s lear [,2]. However, geeral real-world stuato, olear mxture of sgals s geerally more prevalet. For olear demxg [6,7], may dffcultes occur ad the lear ICA s o loger applcable because of the complexty of olear parameters. I ths paper, we shall -deep vestgate a specal but mportat stace of olear mxtures,.e., post-olear (PNL) mxtures, ad gve out a ovel algortthm. 2 Post-olear xtures A mportat specal case of the geeral olear mxg model that cossts of so called post-olear mxtures troduced by Taleb ad Jutte [], ca be see as a hybrd of a lear stage followed by a olear stage. * Ths work was supported by the Natoal Scece Foudato of Cha (Nos.6472, ad 642). J. Wag et al. (Eds.): ISNN 26, LNCS 397, pp. 6 7, 26. Sprger-Verlag Berl Hedelberg 26

2 66 C.-H. Zheg et al. s u f x g v y s A u f x g v B y xg system Separatg system Fg.. The mxg separatg system for PNL I the post-olear mxtures model, the observatos x = ( x, x2, L, x ) T have the followg specfc form (as show Fg. ) x = f ajsj, =, L, () j= The correspodg vector-matrx form ca be wrtte as: x = f ( As ) (2) Cotrary to geeral olear mxtures, the PNL mxtures have a favorable separablty property. I fact, f the correspodg separatg model for post-olear mxtures, as show Fgure, are wrtte as: y = b g ( x ) (3) j j j j= The t ca be demostrated that [], uder weak codtos o the mxg matrx A ad o the source dstrbuto, the output depedece ca be obtaed f ad oly f =, L,., h = g o f are lear. For more detals, please refer to lteratures []. 3 Cotrast Fucto I ths paper, we use Shao s mutual formato as the measure of mutual depedece. It ca be defed as: I( y) = H( y ) H( y ) (4) where H( y) = p( y)log p( y) dy deotes Shao s dfferetal etropy. Accordg to the theory gve above, the separatg system of PNL we proposed ths paper s show Fg.2, where B ad g form the umxg structure for PNL, y are the extracted depedet compoets, ad ψ some olear mappgs, whch are used oly for the optmzato of the etwork.

3 Nolear Bld Source Separato Usg Hybrd Neural Networks 67 Assume that each fucto ψ ( φ, y) s the cumulatve probablty fucto (CPF) of the correspodg compoet y, the z are uformly dstrbuted [, ], Cosequetly, H( z ) = [7]. oreover, because ψ ( φ, y) are all cotuous ad mootoc creasg trasformatos (thus also vertble), the t ca be easly show that I( z) = I( y )[7]. Cosequetly, we ca obta I( y) = I( z) = H( z ) H( z) = H( z ) () Therefore, maxmzg H ( z ) s equvalet to mmzg I( y ). x ψ g B y ψ z x 2 y 2 ψ 2 z 2 g 2 x y ψ z g Fg. 2. The partcular structure of the umxg etwork It has bee proved the lterature [7] that, gve the costrats placed o ψ ( φ, y), the z s bouded to [, ], ad gve that ψ ( φ, y) s also costraed to be a cotuous creasg fucto, the maxmzg H( z ) wll lead ψ ( φ, y) to become the estmates of the CPFs of y. Cosequetly, y should be the duplcate of s wth just sg ad scale ambguty. Now, the fudametal problem that we should to solve s to optmze the etworks (formed by the g, B ad ψ blocks) by maxmzg H ( z ). 4 Usupervsed Learg of Separatg System Wth respect to the separato structure of ths paper, the jot probablstc desty fucto (PDF) of the output vector z ca be calculated as: p( x) p( z) = (6) det( B) g' ( θ, x ) ψ ' ( φ, y ) = = whch leads to the followg expresso of the jot etropy: ( ) ( ψ ) H( z ) = H( x ) + log det( B ) + E log g' ( θ, x ) + E log ' ( φ, y ) (7) = =

4 68 C.-H. Zheg et al. The mmzato of I( y ), whch s equal to maxmze H ( z ) here, requres the computato of ts gradet wth respect to the separato structure parameters B, θ ad φ. I ths paper, we use RBF [3,4] etwork to model the olear parametrc fuctos gk( θ k, xk), ad choose Gaussa kerel fucto as the actvato fucto of the hdde euros. I order to mplemet the costrats o the ψ fucto easy, we use multlayer perceptro to model the olear parametrc fuctos ψ ( φ, y ). k k k Expermet Results. Extractg Sources From xtures of Smulat Sgals I the frst expermet, the source sgals cosst of a susod sgal ad a fuy. curve sgal [],.e. s ( t ) = [(rem(t,27)-3)/9,((rem(t,23)-)/9) ] T, whch are show Fg.3 (a). The two source sgals are frst learly mxed wth the (radomly chose) mxture matrx: A = (8) The, the two olear dstorto fuctos f ( u) = f ( u) = tah( u) (9) 2 are appled to each mxture for producg a PNL mxture. Fg.3 (b) shows the separated sgals. To compare the performace of our proposed method wth other oes, we also use ISEP method [7] to coduct the related expermets based o the same data. The correlatos betwee the two recovered sgals separated by two methods ad the two orgal sources are reported Table.. Clearly, the separated sgals usg the method proposed ths paper s more smlar to the orgal sgals tha the other (a) - - (b) Fg. 3. The two set of sgals show. (a) Source sgals. (b) Separated sgals.

5 Nolear Bld Source Separato Usg Hybrd Neural Networks 69 Table. Correlatos betwee two orgal sources ad the two recovered sgals Expermet smulat sgals speech sgals y y 2 y y 2 ISEP S S ethod S ths paper S Extractg Sources from xtures of Speech Sgals To test the valdty of the algorthm proposed ths paper ulterorly, we also have expermetalzed usg real-lfe speech sgals. I ths expermet two speech sgals (wth 3 samples, samplg rate 8kHz, obtaed from /~relly/ kamra /d8.htm) are post-olearly mxed by: A= () 3 3 f( u) = ( u+ u ), f2 ( u) = u+ tah( u) () 2 6 The expermetal results are show Fg.4 ad Table., whch coforms the cocluso draw from the frst expermet. - - (a) (b) Fg. 4. The two set of speech sgals show. (a) Source sgals. (b) Separated sgals..3 Trag Speed We also performed tests whch we compared, o the same post-olear BSS problems, etworks whch the g blocks had LP structures. Table 2 shows the meas ad stadard devatos of epochs requred to reach the stop crtero, whch was based o the value of the objectve fucto H ( z ), for LP-based etworks ad RBF-based etworks.

6 7 C.-H. Zheg et al. Table 2. Comparso of trag speeds betwee LP-based ad RBF-based etworks Two superg Superg. ad subg. supergaussos RBF LP RBF LP ea St. dev From the two tables we ca see that the separatg results of the two methods are very smlar, but the RBF-based mplemetatos traed faster ad show a smaller oscllato of trag tmes (Oe epoch took approxmately the same tme both kds of etwork). Ths maly caused by the local character of RBF etworks. 6 Coclusos We proposed ths paper a ovel algorthm for post-olear bld source separato. Ths ew method works by optmzg a etwork wth a specalzed archtecture, usg the output etropy as the objectve fucto, whch s equvalet to the mutual formato crtero but eeds ot to calculate the margal etropy of the output. Fally, the expermetal results showed that ths method s compettve to other exstg oes. Refereces. Hyväre, A., Karhue, J., Oja, E.: Idepedet Compoet Aalyss. J. Wley, New York (2) 2. Hyväre, A., Pajue, P.: Nolear Idepedet Compoet Aalyss: Exstece ad Uqueess Results. Neural Networks, 2(3) (999) Huag, D.S.: Systematc Theory of Neural Networks for Patter Recogto. Publshg House of Electroc Idustry of Cha, Bejg (996) 4. Huag, D.S.: The Uted Adaptve Learg Algorthm for the Lk Weghts ad the Shape Parameters RBFN for Patter Recogto. Iteratoal Joural of Patter Recogto ad Artfcal Itellgece.(6) (997) Taleb, A., Jutte, C.: Source Separato Post- olear xtures. IEEE Tras. Sgal Processg, 47 (999) artez, Bray, D. A.: Nolear Bld Source Separato Usg Kerels. IEEE Tras. Neural Networks, 4() (23) Almeuda, L. B.: ISEP Lear ad Nolear ICA Based o utual Iformato. Joural of ache Learg Research.4(2) (23)