Batch and Online Underdetermined Source Separation Using Laplacian Mixture Models

Mitianoudis, Nikolaos; Stathaki, Tania

doi:10.1109/tasl.2007.899281

Cited by 27 publications

(39 citation statements)

References 28 publications

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“…In fact, it has been reported that the Laplacian distribution is able to model speech both in the time domain and in the STFT domain [14]. Moreover, due to the peaky nature of the magnitude ratio distribution, Laplacian mixture models have also been employed in underdetermined blind source separation problems [15]. However, while the Laplacian distribution might be suitable to model STFT coefficients under some circumstances [16], the proposed Cauchy-based model for STFT coefficients has been shown to provide better accuracy in our R-D modeling task.…”

Section: A Suitability Of the Modelmentioning

confidence: 99%

Maximum a Posteriori Binary Mask Estimation for Underdetermined Source Separation Using Smoothed Posteriors

Cobos

Lopez

2012

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Sound source separation has become a topic of intensive research in the last years. The research effort has been specially relevant for the underdetermined case, where a considerable number of sparse methods working in the time-frequency (T-F) domain have appeared. In this context, although binary masking seems to be a preferred choice for source demixing, the estimated masks differ substantially from the ideal ones. This paper proposes a Maximum a Posteriori (MAP) framework for binary mask estimation. To this end, class-conditional source probabilities according to the observed mixing parameters are modeled via ratios of dependent Cauchy distributions while source priors are iteratively calculated from the observed histograms. Moreover, spatially smoothed posteriors in the T-F domain are proposed to avoid noisy estimates, showing that the estimated masks are closer to the ideal ones in terms of objective performance measures.

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Section: A Suitability Of the Modelmentioning

confidence: 99%

Maximum a Posteriori Binary Mask Estimation for Underdetermined Source Separation Using Smoothed Posteriors

Cobos

Lopez

2012

IEEE Trans. Audio Speech Lang. Process.

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“…In particular, Laplacian mixture models (LMMs) have been proposed and applied for the purposes of robust clustering and overcomplete source separation [6,14]. Among robust clustering methods [11,10], those based on LMMs provide simple learning algorithms similar to the learning of Gaussian mixture models (GMMs).…”

Section: Introductionmentioning

confidence: 99%

Vector quantization based on ε-insensitive mixture models

Watanabe

2015

Neurocomputing

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a b s t r a c tLaplacian mixture models have been used to deal with heavy-tailed distributions in data modeling problems. We consider an extension of Laplacian mixture models, which consists of ε-insensitive component distributions. An EM-type learning algorithm is derived for the maximum likelihood estimation of the proposed mixture model. The E-step is formulated in the usual way, while the M-step is formulated as the dual optimization problem instead of the primal optimization problem.Additionally, the convergence proof for ε¼0 is accomplished. As an analogy to the k-means algorithm, we obtain what we call the ei-means algorithm in a certain limit of the learning algorithm. The derived algorithm is applied to approximate computation of rate-distortion functions associated with the ε-insensitive loss function. Then, it is demonstrated by synthetic data and real-world Spambase data that with appropriate selection of the ε value, the model is able to tolerate small percentage of noisy data.

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“…In [3], the DOA (based on AVS) and the mixing vector cues (as in [4]) were combined to achieve separation in reverberant environments, where these cues are modelled by (complex) Gaussian distributions. In anechoic situations, however, a Laplacian distribution has been shown to perform well for modelling the mixing vectors [5].…”

Section: Introductionmentioning

confidence: 99%

Acoustic vector sensor based speech source separation with mixed Gaussian-Laplacian distributions

Chen

Alinaghi

Zhong

et al. 2013

2013 18th International Conference on Digital Signal Processing (DSP)

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Abstract-Acoustic vector sensor (AVS) based convolutive blind source separation problem has been recently addressed under the framework of probabilistic time-frequency (T-F) masking, where both the DOA and the mixing vector cues are modelled by Gaussian distributions. In this paper, we show that the distributions of these cues vary with room acoustics, such as reverberation. Motivated by this observation, we propose a mixed model of Laplacian and Gaussian distributions to provide a better fit for these cues. The parameters of the mixed model are estimated and refined iteratively by an expectation-maximization (EM) algorithm. Experiments performed on the speech mixtures in simulated room environments show that the mixed model offers an average of about 0.68 dB and 1.18 dB improvements in signal-to-distotion (SDR) over the Gaussian and Laplacian model, respectively. Index Terms-Acoustic vector sensor, mixed model, direction of arrival, EM algorithm, blind source separation.

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Batch and Online Underdetermined Source Separation Using Laplacian Mixture Models

Cited by 27 publications

References 28 publications

Maximum a Posteriori Binary Mask Estimation for Underdetermined Source Separation Using Smoothed Posteriors

Maximum a Posteriori Binary Mask Estimation for Underdetermined Source Separation Using Smoothed Posteriors

Vector quantization based on ε-insensitive mixture models

Acoustic vector sensor based speech source separation with mixed Gaussian-Laplacian distributions

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