Independent vector analysis followed by HMM-based feature enhancement for robust speech recognition

Cho, Ji-Won; Park, Hyung‐Min

doi:10.1016/j.sigpro.2015.09.002

Cited by 20 publications

(5 citation statements)

References 27 publications

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Section: Independent Component Analysismentioning

confidence: 99%

“…(39), it is clear that maximizing negentropy is related to minimizing mutual information and they differ only by a sign and a constant C. Moreover, non-Gaussianity measures enable the deflationary (one-by-one) estimation of the ICs which is not possible with mutual information or likelihood approaches. 8 Further, with the non-Gaussianity approach, Independent component analysis all signals are enforced to be uncorrelated, while this constraint is not necessary using mutual information approach.…”

Section: Minimization Of Mutual Informationmentioning

confidence: 99%

“… Audio signal processing: ICA has been widely used in audio signals for removing noise [36]. Additionally, ICA was used as a feature extraction method to design robust automatic speech recognition models [8]. Biometrics: ICA is for extracting discriminative features in different biometrics such as face recognition [10], ear recognition [35], and finger print [27].…”

Section: Applicationsmentioning

confidence: 99%

“…(19) 7. Two vectors x and y are orthonormal if they are orthogonal, i.e., the dot product x:y ¼ 0, and they are unit vectors, i.e., ðxÞ ¼ ðyÞ ¼ 1 8. Maximum Likelihood approach will be introduced in the next section 9.…”

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confidence: 99%

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Independent component analysis: An introduction

Tharwat

2020

ACI

129

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Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details. Therefore, in this paper, the basics of ICA are provided to show how it works to serve as a comprehensive source for researchers who are interested in this field. This paper starts by introducing the definition and underlying principles of ICA. Additionally, different numerical examples in a step-by-step approach are demonstrated to explain the preprocessing steps of ICA and the mixing and unmixing processes in ICA. Moreover, different ICA algorithms, challenges, and applications are presented.

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Section: Independent Component Analysismentioning

confidence: 99%

Section: Minimization Of Mutual Informationmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Independent component analysis: An introduction

Tharwat

2020

ACI

129

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show abstract

“…Independent component analysis (ICA) is a higher-order statistical model-based signal processing method that can separate statistically independent source signals from a set of observed signals without any prior knowledge [23]. ICA has been widely applied to blind source separation (BSS) fields such as speech separation [24], biomedical signals analysis [25], and facial recognition [26,27]. As a novel unmixing method that can recover the original information from observed overlapping mixtures, ICA has attracted great interest in spectral analytical chemistry [28].…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of Atmospheric Gaseous Pollutants Using Fourier-Transform Infrared Spectroscopy Combined with Independent Component Analysis

Liu

et al. 2020

Journal of Spectroscopy

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Fourier-transform infrared (FTIR) spectroscopy is a rapid and nondestructive technology for monitoring atmospheric quality. The identification of each component from the FTIR spectra is a prerequisite for the accurate quantitative analysis of gaseous pollutants. Due to the overlap of different gas absorption peaks and the interference of water vapor in the actual measurement, the existing identification methods of gas spectra have drawbacks of low identification rate and the inability to carry out real-time online analysis in atmospheric quality monitoring. In this work, independent component analysis (ICA) is applied to the spectral separation of heavily overlapped spectra of gaseous pollutants. The proposed method is validated by the analysis of mixture spectra obtained in laboratory and actual atmospheric spectra collected from stationary source. The average time consumption of separation process is less than 0.2 seconds, and the identification rate of experimental gases is up to 100%, as shown by the results of peak searching and the analysis of the correction coefficient between the separated spectra and the standard spectra database. The identification results of actual atmospheric spectra demonstrated that the proposed method can effectively identify the gaseous pollutants whose concentration changes in the measured spectra, and it is a promising qualitative spectral analysis tool that can shorten the identification time, as well as increase the identification rate. Therefore, this method can be a useful alternative to traditional qualitative identification methods for real-time online atmospheric pollutant detection.

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Neural Network Models Ensembles for Generalized Analysis of Audit Data Transformations

Neskorodieva

Fedorov

2022

Mathematical Modeling and Simulation of Systems

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Independent vector analysis followed by HMM-based feature enhancement for robust speech recognition

Cited by 20 publications

References 27 publications

Independent component analysis: An introduction

Independent component analysis: An introduction

Rapid Identification of Atmospheric Gaseous Pollutants Using Fourier-Transform Infrared Spectroscopy Combined with Independent Component Analysis

Neural Network Models Ensembles for Generalized Analysis of Audit Data Transformations

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