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Multichannel Extensions of Non-Negative Matrix ... - IEEE Xplore

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10Computationally efficient implementations of the multichannelNMF algorithms, such as one using a general-purpose graphicsprocessing unit (GPGPU), will also constitute future work.APPENDIX ISOLVING AN ALGEBRAIC RICCATI EQUATIONFig. 11. Source separation performance evaluated in terms of SDRs averagedover all the three sources. With NMF-based methods (EU-NMF and IS-NMF), ten trials were conducted for each mixture ID. The error bars representone standard deviation. For comparison, the results obtained with an existingunderdetermined blind source separation method [26] (UBSS) are also shown.Averaged SDR (dB)76543210EU−NMFIS−NMF0 5 10 15 20 25 30L initin top−down clusteringFig. 12. Source separation performance with various numbers of redundantspatial properties L init . Ten trials were conducted for each mixture ID, andthus the error bars represent one standard deviation over 40 averaged SDRs.that even a small number of redundant spatial propertiescontributed to a better separation result than not having theredundancy (L init =3). Having too much redundancy alsoworked well, but the computational demands also increased.Therefore, we employed L init =9for the experiments whoseresults are shown in Fig. 11.VI. CONCLUSIONWe have presented new formulations and algorithms formultichannel NMF. We started with a simple model (17)where each NMF basis t ik , i =1,...,I, has its own spatialproperties H ik . Then, to cluster the NMF bases accordingto their spatial properties, we introduced the second model(40), which includes cluster-indicator variables Z. Multiplicativeupdate rules were derived to minimize the multichannelIS divergence (16) or multichannel Euclidean distance(20). Experimental results show that the derived multichannelalgorithms were as efficient as the standard single-channelalgorithms in terms of the number of iterations to converge.Multichannel NMF with IS divergence produced better sourceseparation results than with Euclidean distance for severalstereo music mixtures. Future work will include the automaticdetermination of such model complexity parameters as thenumber of NMF bases K and sources (spatial properties) L,for example by employing Bayesian nonparametrics [28], [29].To solve (24), we perform an eigenvalue decomposition ofa 2M × 2M matrix[ ]0 −A(56)−B 0and let e 1 ,...,e M be eigenvectors with negative eigenvalues.It is theoretically guaranteed that there are exactly M negativeeigenvalues. But in reality there may be computer arithmeticerrors. Thus, we actually sort the 2M eigenvectors accordingto the corresponding eigenvalues in ascending order andemploy the first M eigenvectors.Then, let us decompose the 2M-dimensional M eigenvectorsas[ ]fme m = for m =1,...,M (57)g mwith f m and g m being M-dimensional vectors. The new H ikis calculated byH ik ← GF −1 (58)with F = [f 1 ,...,f M ] and G = [g 1 ,...,g M ]. Again tocompensate for computer arithmetic errors, we ensure H ik isHermitian by H ik ← 1 2 (H ik + H H ik ).APPENDIX IIPROOF FOR THE AUXILIARY FUNCTION (29)Let us consider the minimization of f + defined in (29) withrespect to R and U subject to the constraint ∑ k R ijk = I. Byintroducing Lagrange multipliers Λ ij of size M × M, wehaveF = f + + ∑ ij Re { tr [ ( ∑ k R ijk − I) H Λ ij]}. (59)By setting the partial derivative of F with respect to R ∗ ijk atzero∂F=(t ik v kj H ik ) −1 R ijk X ij + Λ ij = 0 , (60)∂R ∗ ijkwe have R ijk = −(t ik v kj H ik )Λ ij X −1ij . Adding this for k =1,...,K gives Λ ij = −ˆX −1ij X ij with the fact that ∑ k R ijk =I. Therefore, the minimum of the auxiliary function f + isobtained whenR ijk = t ik v kj H ˆX−1 ik ij (61)and the minimum value is equal to f defined in (28).The partial derivative of f + with respect to Hermitianmatrix U ∗ ij is given by [30]∂f +∂U ∗ ij= U −1ijSetting this zero gives U ij = ˆX ij .− det ˆX ijdet U ijU −1ij . (62)

11APPENDIX IIIPROOF FOR THE AUXILIARY FUNCTION (35)Let us consider the minimization of f + defined in (35)with respect to R subject to the constraint ∑ k R ijk = I. Byintroducing Lagrange multipliers Λ ij of size M × M, wehaveF = f + + ∑ ij Re { tr [ ( ∑ k R ijk − I) H Λ ij]}. (63)The partial derivative of F with respect to Hermitian matrixR ∗ ijk is given by [30]∂F∂R ∗ ijk= −t 2 ikv 2 kjR −1ijk HH ikH ik R −1ijk + Λ ij . (64)Setting this zero and introducing a matrix U ij we haveΛ ij =(t ik v kj R −1ijk H ik)(t ik v kj R −1ijk H ik) H = U ij U H ij . (65)A solution for this equation is given byU ij = t ik v kj R −1ijk H ik ⇔ R ijk = t ik v kj H ik U −1ij , (66)and adding this for k =1,...,K givesU ij = ∑ k H ikt ik v kj = ˆX ij (67)with the fact that ∑ k R ijk = I. Therefore, the minimum ofthe auxiliary function f + is obtained whenR ijk = t ik v kj H ˆX−1 ik ij(68)and the minimum value is equal to f defined in (34).APPENDIX IVEM ALGORITHM FOR COMPARISONThis appendix shows an EM algorithm designed to minimizethe total multichannel IS divergence, (18) with (16),according to the NMF model (17). The EM algorithm shownhere is a simplification of the EM algorithm shown in [16].For the STFT coefficient vectors ˜x ij ∈ C M , let ỹ ijk ∈ C Mbe latent vectors that satisfy ˜x ij = ∑ k ỹijk.1) E-step: calculate the expectation of the outer product ofỹ ijk byE[ỹ ijk ỹ H ijk ]=Ŷijk + Ŷijk(ˆX−1 ij X −1ij ˆXij−)−1 ˆXij Ŷijk (69)withˆX ij = ∑ Ŷ ijk , Ŷ ijk = H ik t ik v kj . (70)k2) M-step: update the NMF model parameters byt ik ← 1 ∑ 1tr ( H −1ikJM v E[ỹ ijkỹijk H ]) , (71)j kjv kj ← 1 ∑ 1tr ( H −1ikIM t E[ỹ ijkỹijk] H ) , (72)i ikH ik ← 1 ∑ 1E[ỹ ijk ỹt ik J vijk] H . (73)j kjREFERENCES[1] H. Sawada, H. Kameoka, S. Araki, and N. Ueda, “New formulations andefficient algorithms for multichannel NMF,” in Proc. WASPAA 2011,Oct. 2011, pp. 153–156.[2] ——, “Efficient algorithms for multichannel extensions of Itakura-Saitononnegative matrix factorization,” in Proc. ICASSP 2012, Mar. 2012,pp. 261–264.[3] D. D. Lee and H. S. Seung, “Learning the parts of objects withnonnegative matrix factorization,” Nature, vol. 401, pp. 788–791, 1999.[4] W. Xu, X. Liu, and Y. Gong, “Document clustering based on nonnegativematrix factorization,” in Proc. ACM SIGIR, 2003, pp. 267–273.[5] P. Smaragdis and J. C. Brown, “Non-negative matrix factorization forpolyphonic music transcription,” in Proc. WASPAA 2003, Oct. 2003, pp.177–180.[6] T. Virtanen, “Monaural sound source separation by nonnegative matrixfactorization with temporal continuity and sparseness criteria,” Audio,Speech, and Language Processing, IEEE Transactions on, vol. 15, no. 3,pp. 1066–1074, 2007.[7] P. Smaragdis, “Convolutive speech bases and their application to supervisedspeech separation,” IEEE Trans. Audio, Speech, and LanguageProcessing, vol. 15, pp. 1–12, 2007.[8] M. Nakano, J. Le Roux, H. Kameoka, Y. Kitano, N. Ono, andS. Sagayama, “Nonnegative matrix factorization with Markov-chainedbases for modeling time-varying patterns in music spectrograms,” LatentVariable Analysis and Signal Separation, pp. 149–156, 2010.[9] G. Mysore, P. Smaragdis, and B. Raj, “Non-negative hidden Markovmodeling of audio with application to source separation,” in LatentVariable Analysis and Signal Separation. Springer, 2010, pp. 140–148.[10] D. Fitzgerald, M. Cranitch, and E. Coyle, “Shifted non-negative matrixfactorisation for sound source separation,” in IEEE/SP 13th Workshopon Statistical Signal Processing, 2005, pp. 1132–1137.[11] R. Jaiswal, D. FitzGerald, D. Barry, E. Coyle, and S. Rickard, “ClusteringNMF basis functions using shifted NMF for monaural sound sourceseparation,” in Proc. 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