2016
DOI: 10.1016/j.specom.2015.07.002
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Computational methods for underdetermined convolutive speech localization and separation via model-based sparse component analysis

Abstract: In this paper, the problem of speech source localization and separation from recordings of convolutive underdetermined mixtures is studied. The problem is cast as recovering the spatio-spectral speech information embedded in a microphone array compressed measurements of the acoustic field. A model-based sparse component analysis framework is formulated for sparse reconstruction of the speech spectra in a reverberant acoustic resulting in joint localization and separation of the individual sources. We compare a… Show more

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Cited by 14 publications
(7 citation statements)
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“…Figure13shows the mixed signals and the histogram for these mixture signals. As shown in the figure, the mixture signals follow all the properties that were mentioned in Section 2.1.1, where (1) source signals are more independent than mixture signals,(2) the histograms of mixture signals in Figure13are much more Gaussian than the histogram of source signals in Figure12mixtures signals (see Figure13are more complex than source signals (see Figure12)).In the projection pursuit algorithm, mixture signals are first whitened, and then the values of the first weight vector ðw 1 Þ are initialized randomly. The value of w 1 is listed in Table1.…”
mentioning
confidence: 88%
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“…Figure13shows the mixed signals and the histogram for these mixture signals. As shown in the figure, the mixture signals follow all the properties that were mentioned in Section 2.1.1, where (1) source signals are more independent than mixture signals,(2) the histograms of mixture signals in Figure13are much more Gaussian than the histogram of source signals in Figure12mixtures signals (see Figure13are more complex than source signals (see Figure12)).In the projection pursuit algorithm, mixture signals are first whitened, and then the values of the first weight vector ðw 1 Þ are initialized randomly. The value of w 1 is listed in Table1.…”
mentioning
confidence: 88%
“…These data can be in the form of images, stock markets, or sounds. Hence, ICA was used for extracting source signals in many applications such as medical signals [7,34], biological assays [3], and audio signals [2]. ICA is also considered as a dimensionality reduction algorithm when ICA can delete or retain a single source.…”
Section: Introductionmentioning
confidence: 99%
“…Nayak and Devulapalli (2016) proposed a cluster-based WSN localization algorithm that uses cluster structure and a global system to represent network distribution, reduces measurement error through multi-hop probability calculation, and improves node positioning accuracy [2]. Asaei et al (2017) proposed a localization method based on perceptual sparse matrix, transforming the distribution of nodes into discrete regions of sparse matrices, and using greedy algorithms and deterministic monitoring matrices to reduce the number of measurements [3]. Sicari (2015) used a global single key to encrypt nodes in the TinSec positioning system.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Then the maximum posterior technique are usually used to estimate parameters. In this section, all the signals are complex valued, which is different from the problem in [132]. We start our study from using the following complex Gaussian distribution to model g,…”
Section: Aligning Dictionaries To Corresponding Channelsmentioning
confidence: 99%
“…The sparseness of three speech signals is illustrated inFigure 6.2. In[132], it is shown that one SBL method realized by a BSBL algorithm could achieve the best perceived speech quality. However,these sparse Bayesian recovery methods are not designed for speech signals in TF domain.…”
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confidence: 99%