Sparse coding with adaptive dictionary learning for underdetermined blind speech separation

Xu, Tao; Wang, Wenwu; Dai, Wei

doi:10.1016/j.specom.2012.12.003

Cited by 27 publications

(7 citation statements)

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“…A block-based approach coupled with adaptive dictionary was presented for underdetermined blind speech separation. e proposed algorithm, derived as a multistage method, was established by reformulating the underdetermined blind source separation problem as a sparse coding problem [18]. A new decentralized modal identification method was proposed using parallel factor decomposition and sparse blind source separation [19].…”

Section: Introductionmentioning

confidence: 99%

A Sparse Underdetermined Blind Source Separation Method and Its Application in Fault Diagnosis of Rotating Machinery

Wang

2020

Complexity

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Rolling element bearing is one of the most commonly used supporting parts in rotating machinery, and it is also one of the most easily failing rotating parts. It is of great safety and economic significance to study the effective fault diagnosis method of rolling element bearing. The fault characteristic signal of rolling bearing is often affected by other interference signals in practical engineering, and the situation is much more serious when the rolling bearing fault occurs in gearbox. Besides, only a limited number of measuring points are used in the process of rolling bearing fault signal acquisition due to the limitation of sensors installation condition. In some sense, the above two factors often cause the result that the fault diagnosis of rolling bearing is the problem of underdetermined blind source separation. The independence and non-Gaussian characteristic of the observed signals are the prerequisite of most of existent blind source separation methods. Unlike traditional blind source separation methods, SCA originating from sparse representation is an effective method to solve the problem of underdetermined blind source separation, because it does not require the independence or non-Gaussian characteristics of the observed signals, and it only makes full use of the sparse characteristics of the observed signals to extract the source signal from the observed signals. Based on these, a sparse component analysis (SCA) method based on linear clustering (LC) named LC-SCA is proposed for the purpose of underdetermined blind source separation of vibration signals of rolling element bearing, and the LC is introduced into SCA to improve the computation efficiency of SCA. The effectiveness of the proposed method is verified by simulation and experiment. In addition, the superiority of the method is verified by comparison with the other related methods such as constrained independent component analysis (cICA) and SCA.

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Section: Introductionmentioning

confidence: 99%

A Sparse Underdetermined Blind Source Separation Method and Its Application in Fault Diagnosis of Rotating Machinery

Wang

2020

Complexity

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“…Blind source separation (BSS) is to recover the sources from the mixtures without the knowledge of the mixing system. BSS is widely applied in speech signal processing, digital communication, machinery diagnosis and so on [1]- [4]. For a linear and instantaneous mixing system with N sources and M mixtures, the BSS problem can be formulated as x(t) = As(t) + n(t),…”

Section: Introductionmentioning

confidence: 99%

Underdetermined Speech Blind Identification Based on Spectrum Correction and Phase Coherence Criterion

Huang

Liu

2019

IEEE Access

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The existing underdetermined speech blind identification (BI) algorithms can hardly possess both high recovery quality and high efficiency. This limitation may lie in the neglection of the phase extraction of speeches, which requires technical innovation of the phase-coherence identification. This paper proposes a BI scheme characterized by a combination of the ratio-interpolation-based spectrum corrector and a phase-coherence criterion (involving the operations of frequency merging, effective candidate pattern screening, and single-active-source (SAS) pattern recognition). Its high recovery quality is due to the combination that yields a set of SAS patterns with accurate harmonic parameters. Its high efficiency arises from two aspects: first, the phase-coherence criterion condenses the original patterns into a small quantity of SAS patterns; and second, an efficient density-based clustering algorithm is adopted to classify these SAS patterns. Essentially, the performance enhancement owns to the fact that the sources' phase information can be effectively extracted from the observations by means of the above technique combinations. Both the theoretical analysis and simulation verified that the proposed BI method outperforms the existing BI algorithms in recovery quality, efficiency, and anti-noise performance, which presents a vast potential in other harmonics-related BSS fields, such as mechanical vibration analysis, and channel estimation in communication.

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“…The regularization term is adopted to exploit the prior information, such as the sparsity of the sources [36,37,40], as considered in [36] for seismic signals, in [10] [21] for spike signals, and in [22] for images. In blind source separation, however, apart from the sparsity that is often assumed for the underdetermined case [28] [43], statistical independence between the sources is also widely exploited for estimating the sources and the mixing channels [9,18,19,29,30,34,39].…”

Section: Introductionmentioning

confidence: 99%

Sparse Blind Speech Deconvolution with Dynamic Range Regularization and Indicator Function

Guan

Wang

2017

Circuits Syst Signal Process

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Blind deconvolution is an ill-posed problem. To solve such a problem, prior information, such as, the sparseness of the source (i.e. input) signal or channel impulse responses, is usually adopted. In speech deconvolution, the source signal is not naturally sparse. However, the direct impulse and early reflections of the impulse responses of an acoustic system can be considered as sparse. In this paper, we exploit the channel sparsity and present an algorithm for speech deconvolution, where the dynamic range of the convolutive speech is also used as the prior information. In this algorithm, the estimation of the impulse response and the source signal is achieved by alternating between two steps, namely, the ℓ 1 regularized least squares optimization and a proximal operation. As demonstrated in our experiments, the proposed method provides superior performance for deconvolution of a sparse acoustic system, as compared with two state-of-the-art methods.

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Sparse coding with adaptive dictionary learning for underdetermined blind speech separation

Cited by 27 publications

References 50 publications

A Sparse Underdetermined Blind Source Separation Method and Its Application in Fault Diagnosis of Rotating Machinery

A Sparse Underdetermined Blind Source Separation Method and Its Application in Fault Diagnosis of Rotating Machinery

Underdetermined Speech Blind Identification Based on Spectrum Correction and Phase Coherence Criterion

Sparse Blind Speech Deconvolution with Dynamic Range Regularization and Indicator Function

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