Blind signal separation method and relationship between source separation and source localisation in the TF plane

Cholnam, Om; Chongil, Gwak; Chol, Rim Kyong

doi:10.1049/iet-spr.2018.5132

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…In the case of only a single microphone being available, this reduces to the single channel blind source separation (SCBSS) [1][2][3][4]. The majority of SCBSS algorithms work in time-frequency domain, for example, binary masking [5][6][7] or nonnegative matrix factorization (NMF) [8][9][10][11]. NMF has been continuously developed with great success for decomposing underlying original signals when a sole sensor is available.…”

Section: Introductionmentioning

confidence: 99%

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

Parathai

Tengtrairat

Woo

et al. 2020

Sensors

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This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

Parathai

Tengtrairat

Woo

et al. 2020

Sensors

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show abstract

“…Some algorithms add a penalty term, e.g.

\det false(B false)

to (4), in order to evade the trivial solution [31, 32]. Some other algorithms constrain each row of the separating matrix (

{bold-italicb}_{i}^{normalT} \in R^{1 \times n}

).…”

Section: Introductionmentioning

confidence: 99%

Geometry‐based solution of joint diagonalisation in blind source separation

Akhavan

Esmaeili

Soltanian‐Zadeh

2019

IET signal process.

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Joint diagonalisation (JD) of a set of target matrices is a common approach to solve the blind source separation (BSS) problem. In fact, the separating matrix (the inverse of the mixing matrix) of the sources is the joint diagonaliser of the target matrices. In this study, the authors show that each row of the separating matrix maps the target matrices using a linear mapping into the new vectors which are located on a direct line along the corresponding column of the mixing matrix. Based on this geometrical interpretation, the authors propose a method for solving JD problem, which let us estimate the rows of the separating matrix and the columns of the mixing matrix, (i) independently and in parallel, (ii) consecutively or (iii) simultaneously. Simulation results in different scenarios such as additive noise, ill-posed mixing matrix, and high-dimensional data demonstrate the effectiveness of the proposed method relative to state-of-the-art JD methods. The authors also propose an approach to omit the effect of outlier data, which severely degrades parameters estimation. The proposed approach can be used in JD algorithms to improve their performance in the presence of outlier data.

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Blind signal separation method and relationship between source separation and source localisation in the TF plane

Cited by 2 publications

References 29 publications

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

Geometry‐based solution of joint diagonalisation in blind source separation

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