Nonlinear single channel source separation

Darsono, A. M.; Gao, Bin; Woo, Wai Lok; Dlay, S.S.

doi:10.1109/csndsp16145.2010.5580373

Cited by 4 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective of NMF is to locate an estimated factorization for a nonnegative matrix V into two nonnegative networks W and H. The columns of W are called basis functions, while the rows of H represent the hidden nonnegative sources that correspond to each basis function. In comparison, NMF is better than other matrix factorization approaches such as principal component analysis (PCA) or independent component analysis (ICA) [3] because nonnegativity constraint in NMF produces sparse results which are advantageous [4], [5]. NMF claims that it can be easily solved by multiplicative update (MU) rules [6]- [16].…”

Section: Introductionmentioning

confidence: 99%

Alpha-divergence two-dimensional nonnegative matrix factorization for biomedical blind source separation

Darsono

Chuan²,

Manap³

et al. 2023

IJECE

View full text Add to dashboard Cite

An alpha-divergence two-dimensional nonnegative matrix factorization (NMF2D) for biomedical signal separation is presented. NMF2D is a popular approach for retrieving low-rank approximations of nonnegative data such as image pixel, audio signal, data mining, pattern recognition and so on. In this paper, we concentrate on biomedical signal separation by using NMF2D with alpha-divergence family which decomposes a mixture into two-dimensional convolution factor matrices that represent temporal code and the spectral basis. The proposed iterative estimation algorithm (alpha-divergence algorithm) is initialized with random values, and it updated using multiplicative update rules until the values converge. Simulation experiments were carried out by comparing the original and estimated signal in term of signal-to-distortion ratio (SDR). The performances have been evaluated by including and excluding the sparseness constraint which sparseness is favored by penalizing nonzero gains. As a result, the proposed algorithm improved the iteration speed and sparseness constraints produce slight improvement of SDR.

show abstract

Section: Introductionmentioning

confidence: 99%

Alpha-divergence two-dimensional nonnegative matrix factorization for biomedical blind source separation

Darsono

Chuan²,

Manap³

et al. 2023

IJECE

View full text Add to dashboard Cite

show abstract

“…If I≥J; then it is the Over-determined case [26]. (iii) Mixture: It is related to the type of mixtures; whether it is linear [17][18][19][20][21] or non-linear [27], and the mixing operation; whether it is convolutive [17,[19][20][21] or instantaneous [28]. The convolutive case is more realistic than the instantaneous one because it considers the reverberation of the channel.…”

Section: Introductionmentioning

confidence: 99%

Underdetermined Convolutive Source Separation Using GEM-MU With Variational Approximated Optimum Model Order NMF2D

Al-Tmeme

Woo

Dlay

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

View full text Add to dashboard Cite

An unsupervised machine learning algorithm based on nonnegative matrix factor 2D deconvolution (NMF2D) with approximated optimum model order is proposed. The proposed algorithm adapted under the hybrid framework that combines the generalized EM algorithm with multiplicative update (GEM-MU). As the number of parameters in the NMF2D grows exponentially as the number of frequency basis increases linearly, the issues of model order fitness, initialization and parameters estimation become ever more critical. This paper proposes a variational Bayesian method to optimize the number of components in the NMF2D by using the Gamma-Exponential process as the observation-latent model. In addition, it is shown that the proposed Gamma-Exponential process can be used to initialize the NMF2D parameters. Finally, the paper investigates the issue and advantages of using different window length. Experimental results for the synthetic convolutive mixtures and live recordings verify the competence of the proposed algorithm.

show abstract

“…These assumptions include prior information about the mixture and the problem dimensionality. A great deal of research assumed a linear [11][12][13][14][15][16][17] or nonlinear mixture, 18 instantaneous 19,20 or convolutive 11,12,[14][15][16][17] mixing process, and the sources are less (underdetermined), [11][12][13][14][15][16][17]21 equal (determined, 22 or greater (overdetermined) 21,23 than the number of microphones.…”

mentioning

confidence: 99%

Underdetermined reverberant acoustic source separation using weighted full-rank nonnegative tensor models

Tmeme

Woo

Dlay

et al. 2015

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

In this paper, a fusion of K models of full-rank weighted nonnegative tensor factor two-dimensional deconvolution (K-wNTF2D) is proposed to separate the acoustic sources that have been mixed in an underdetermined reverberant environment. The model is adapted in an unsupervised manner under the hybrid framework of the generalized expectation maximization and multiplicative update algorithms. The derivation of the algorithm and the development of proposed full-rank K-wNTF2D will be shown. The algorithm also encodes a set of variable sparsity parameters derived from Gibbs distribution into the K-wNTF2D model. This optimizes each sub-model in K-wNTF2D with the required sparsity to model the time-varying variances of the sources in the spectrogram. In addition, an initialization method is proposed to initialize the parameters in the K-wNTF2D. Experimental results on the underdetermined reverberant mixing environment have shown that the proposed algorithm is effective at separating the mixture with an average signal-to-distortion ratio of 3 dB.

show abstract

Nonlinear single channel source separation

Cited by 4 publications

References 9 publications

Alpha-divergence two-dimensional nonnegative matrix factorization for biomedical blind source separation

Alpha-divergence two-dimensional nonnegative matrix factorization for biomedical blind source separation

Underdetermined Convolutive Source Separation Using GEM-MU With Variational Approximated Optimum Model Order NMF2D

Underdetermined reverberant acoustic source separation using weighted full-rank nonnegative tensor models

Contact Info

Product

Resources

About