Fast Online Source Steering Algorithm for Tracking Single Moving Source Using Online Independent Vector Analysis

Nakashima, Taishi; Ikeshita, Rintaro; Araki, Shoko; Nakatani, Tomohiro

doi:10.1109/icassp49357.2023.10094962

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2024

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Cited by 3 publications

References 28 publications

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A Computationally Efficient Semi-Blind Source Separation Approach for Nonlinear Echo Cancellation Based on an Element-Wise Iterative Source Steering

Lu,

Wang,

Ueda

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A Computationally Efficient Semi-Blind Source Separation Approach for Nonlinear Echo Cancellation Based on an Element-Wise Iterative Source Steering

Lu,

Wang,

Ueda

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

On Semi-Blind Source Separation-Based Approaches to Nonlinear Echo Cancellation Based on Bilinear Alternating Optimization

Wang,

Yang,

Brendel

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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An Efficient Analytic Solution for Joint Blind Source Separation

Gabrielson,

Baker Siddique Akhonda,

Lehmann

et al. 2024

IEEE Trans. Signal Process.

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Joint blind source separation (JBSS) is a powerful methodology for analyzing multiple related datasets, able to jointly extract sources that describe statistical dependencies across the datasets. However, JBSS can be computationally prohibitive with high-dimensional data, thus there exists a key need for more efficient JBSS algorithms. JBSS algorithms typically rely on numerical solutions, which may be expensive due to their iterative nature. In contrast, analytic solutions follow consistent procedures that are often less expensive. In this paper, we introduce an efficient analytic solution for JBSS. Denoting a set of sources dependent across the datasets as a "source component vector" (SCV), our solution minimizes correlation among separate SCVs by minimizing distance of the SCV cross-covariance's eigenvector matrix from a block diagonal matrix. Under the orthogonality constraint, this leads to a system of linear equations wherein each subproblem has an analytic solution. We derive identifiability conditions of our solution's estimator, and demonstrate estimation performance and time efficiency in comparison with other JBSS algorithms that exploit source correlation across datasets. Results demonstrate that our solution achieves the lowest asymptotic computational complexity among JBSS algorithms, and is capable of superior estimation performance compared with algorithms of similar complexity.

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Fast Online Source Steering Algorithm for Tracking Single Moving Source Using Online Independent Vector Analysis

Cited by 3 publications

References 28 publications

A Computationally Efficient Semi-Blind Source Separation Approach for Nonlinear Echo Cancellation Based on an Element-Wise Iterative Source Steering

A Computationally Efficient Semi-Blind Source Separation Approach for Nonlinear Echo Cancellation Based on an Element-Wise Iterative Source Steering

On Semi-Blind Source Separation-Based Approaches to Nonlinear Echo Cancellation Based on Bilinear Alternating Optimization

An Efficient Analytic Solution for Joint Blind Source Separation

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