Structured sparse signal models and decomposition algorithm for super-resolution in sound field recording and reproduction

Koyama, Shoichi; Murata, Naoki; Saruwatari, Hiroshi

doi:10.1109/icassp.2015.7178043

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…These variables can also be related as a linear equation as in (5). We previously proposed an algorithm for solving the group sparse decomposition problem by extending M-FOCUSS [10], [11].…”

Section: Combined Modelsmentioning

confidence: 98%

“…We compared group SBL (G-SBL) with M-SBL [13], group FOCUSS (G-FOCUSS) [10], and M-FOCUSS [11] using randomly generated signals and dictionaries. Two different combinations models, models 1 and 2 and model 1 and 3, were investigated.…”

Section: A Comparison Of Algorithms Using Synthetic Datamentioning

confidence: 99%

“…We have proposed three different group sparse signal models based on the physical properties of the sound field [10]. To address these signal models, an algorithm extended from the M-FOCUSS algorithm [11] was applied in [10]. In this paper, we extend the algorithm called sparse Bayesian learning (SBL) [12], [13] so that the proposed group sparse signal models can be addressed.…”

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

“…We previously developed a signal conversion method based on sparse sound field decomposition to reduce the effect of spatial aliasing artifacts [4], [10], in which the sound field is modeled as the sum of monopole source and plane wave components. Since only a few monopole components may exist inside a region near the microphones, it is possible to sparsely decompose the observed signals into basis functions, or dictionaries, consisting of Green's function.…”

Section: Introductionmentioning

confidence: 99%

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Sparse sound field decomposition using group sparse Bayesian learning

Koyama

Matsubayashi

Murata

et al. 2015

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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A sparse sound field decomposition method is proposed. Sound field decomposition is the foundation of the various acoustic signal processing applications and enables the estimation of the entire sound field from pressure measurements. The plane wave decomposition, i.e., spatial Fourier analysis, of the sound field has been widely used; however, artifacts originating from spatial aliasing occur above the spatial Nyquist frequency. We have proposed a sparse sound field decomposition method based on a generative model as a sum of monopole source and plane wave components in the context of sound field recording and reproduction. For more accurate and robust decomposition, we propose three different group sparse signal models based on physical properties and a decomposition algorithm by extending sparse Bayesian learning. In simulation experiments, the accuracy of sparse decomposition was improved compared with that of current methods.

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Section: Combined Modelsmentioning

confidence: 98%