A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation

Guo, Ruiming; Luo, Zhi-Quan; Li, Mingchun

doi:10.3390/s23010493

Cited by 13 publications

(7 citation statements)

References 58 publications

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“…The paper [26] discusses the speech extraction models based on different DNNs using audio, video, spatial, or voice clues of the target. The work in [27] reviews only the source separation application of AE using independent vector analysis. The review in [28] is focused only on machine learning and deep learning models used for AE in hearing aids.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net

Gul,

Khan

2023

IEEE Access

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The recent surge in the use of Deep Neural Networks (DNNs) has also made its mark in the field of Audio Enhancement (AE), providing much better quality than the classical methods. Although, there are dedicated audio processing DNNs, yet, many recent models of AE have utilized U-Net: a DNN based on Convolutional Neural Network (CNN), fundamentally developed for image segmentation. It is found that the useful features hidden in the time domain are highlighted when the audio signal is converted to a spectrogram, which can be treated as an image. In this article, we will review the recent work, utilizing U-Nets for different AE applications. Different than other published reviews, this review focuses entirely on AE techniques based on image U-Nets. We will discuss the need for AE, U-Net comparison to other DNNs, the benefits of converting the audio to 2D, input representations that are useful for different AE applications, the architecture of vanilla U-Net and the pre-trained models, variations in vanilla architecture incorporated in different E models, and the state-of-the-art AE algorithms based on U-Net in various applications. Apart from speech and music, this article discusses a wide range of audio signals e.g. environmental, biomedical, bioacoustics, and industrial sounds, not covered collectively in a single article in previously published studies. The article ends with the discussion of colored spectrograms in future AE applications.INDEX TERMS CNNs; image processing deep neural networks; pre-trained networks; spectrogram.;U-Net.

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Section: Introductionmentioning

confidence: 99%

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net

Gul,

Khan

2023

IEEE Access

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“…In this paper we deal with another very common technique in BSS known as Independent Component Analysis (ICA). This method and its related Independent Vector Analysis (IVA) are widely used in all kinds of areas requiring signal extraction 19 . The ICA method depends on the separation of independent components (i.e., sources) and relies on the assumption of their statistical independence.…”

Section: Introductionmentioning

confidence: 99%

Fast gradient algorithm for complex ICA and its application to the MIMO systems

Mika

2023

Sci Rep

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This paper proposes a new gradient-descent algorithm for complex independent component analysis and presents its application to the Multiple-Input Multiple-Output communication systems. Algorithm uses the Lie structure of optimization landscape and toral decomposition of gradient matrix. The theoretical results are validated by computer simulation and compared to several classes of algorithms, gradient descent, quasi-Newton as well as complex JADE. The simulations performed showed excellent results of the algorithm in terms of speed, stability of operation and the quality of separation. A characteristic feature of gradient methods is their quick response to changes in the input signal. The good results of the proposed algorithm indicate potential use in on-line applications.

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“…Blind source separation (BSS) [1] refers to unmixing or extracting the latent sources from the observed mixed signals with minimal prior information. It has become a versatile technology with diverse applications, such as in speech signals [2,3], biomedical signals [4] and digital communication signals [5,6]. Independent component analysis (ICA) [7,8] is one of the most basic means proposed to deal with BSS.…”

Section: Introductionmentioning

confidence: 99%

Efficient Overdetermined Independent Vector Analysis Based on Iterative Projection with Adjustment

Guo

Luo

2023

Electronics

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In this paper, a computationally efficient optimization algorithm for independent vector analysis (IVA) is proposed to accelerate iterative convergence speed and enhance the overdetermined convolutive blind speech separation performance. An iterative projection with adjustment (IPA) is investigated to estimate the unmixing matrix for OverIVA. The IPA algorithm jointly executes the iterative projection (IP) algorithm and the iterative source steering (ISS) algorithm to jointly update one row and one column of the mixing matrix, which can perform computationally-efficient blind source separation. It is achieved by updating one demixing filter and jointly adjusting all the other sources along its current direction. Motivated by its technology superiorities, this paper proposes a modified algorithm for the OverIVA, fully exploiting the computational efficiency of IPA optimization scheme. Experimental results corroborate the proposed OverIVA-IPA algorithm converges faster and performs better than the existing state-of-the-arts algorithms.

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A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation

Cited by 13 publications

References 58 publications

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net

Fast gradient algorithm for complex ICA and its application to the MIMO systems

Efficient Overdetermined Independent Vector Analysis Based on Iterative Projection with Adjustment

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