Blind and Semi-blind Anechoic Mixing System Identification Using Multichannel Matching Pursuit

Haddad, Diego B.; Lovisolo, Lisandro; Petraglia, M.R.; Batalheiro, P.B.; Filho, Jorge Costa Pires

doi:10.1007/s00034-021-01681-1

Cited by 3 publications

(1 citation statement)

References 71 publications

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“…In addition to the improvement of the above steps, the algorithm is improved from other angles. For example, in 2021, Haddad et al [71] expressed the mixed signal as a linear combination of delay components selected from the over complete dictionary by using the spatial constraints between different channels, which can greatly reduce the impact of delay on separation.…”

Section: ) Source Signal Separationmentioning

confidence: 99%

Advances in Time-Frequency Analysis for Blind Source Separation: Challenges, Contributions, and Emerging Trends

Li,

Ramli

2023

IEEE Access

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Blind source separation (BSS) is a critical task in untangling non-stationary signals without prior information. This paper extensively explores diverse time-frequency analysis (TFA) methods within BSS systems over the past decade. It underscores the pivotal role of TFA in dealing with non-stationary signals by characterizing their attributes across time and frequency domains. This approach provides a comprehensive understanding of signal dynamics that surpasses conventional techniques focusing solely on temporal or spectral domains. The paper delves into various TFA methods, investigating their influencing factors and aiding researchers in selecting relevant techniques aligned with their objectives. Furthermore, it comprehensively reviews contemporary research, categorizing BSS algorithms into three classes. The role of commonly used TFA methods in each class is systematically evaluated, identifying their strengths and limitations during different separation stages. The paper addresses challenges in implementing BSS algorithms, particularly in under-determined systems with fewer mixing channels than source signals. It highlights the central role of TFA in overcoming these challenges and enhancing separation outcomes.

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Section: ) Source Signal Separationmentioning

confidence: 99%

Advances in Time-Frequency Analysis for Blind Source Separation: Challenges, Contributions, and Emerging Trends

Li,

Ramli

2023

IEEE Access

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Underdetermined Blind Source Separation Based on Spatial Estimation and Compressed Sensing

Wei,

Zhang

2023

Circuits Syst Signal Process

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Technique for Kernel Matching Pursuit Based on Intuitionistic Fuzzy c-Means Clustering

Lei,

Zhang

2024

Electronics

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Kernel matching pursuit (KMP) requires every step of the searching process to be global optimal searching in the redundant dictionary of functions in order to select the best matching signal structure. Namely, the dictionary learning time of KMP is too long. To solve the above drawbacks, a rough dataset was divided into some small-sized dictionaries to substitute local searching for global searching by using the property superiority of dynamic clustering performance, which is also superior in the intuitionistic fuzzy c-means (IFCM) algorithm. Then, we proposed a novel technique for KMP based on IFCM (IFCM-KMP). Subsequently, three tests including classification, effectiveness, and time complexity were carried out on four practical sample datasets, the conclusions of which fully demonstrate that the IFCM-KMP algorithm is superior to FCM and KMP.

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Blind and Semi-blind Anechoic Mixing System Identification Using Multichannel Matching Pursuit

Cited by 3 publications

References 71 publications

Advances in Time-Frequency Analysis for Blind Source Separation: Challenges, Contributions, and Emerging Trends

Advances in Time-Frequency Analysis for Blind Source Separation: Challenges, Contributions, and Emerging Trends

Underdetermined Blind Source Separation Based on Spatial Estimation and Compressed Sensing

Technique for Kernel Matching Pursuit Based on Intuitionistic Fuzzy c-Means Clustering

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