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Faster independent vector analysis with joint pairwise updates of demixing vectors

ARTIFICIAL INTELLIGENCE REVIEW 2024年第2期58卷 1-18页

作者： Luo, Zhongqiang Guo, Ruiming Wang, Ling Sichuan Univ Sci & Engn Sch Automat & Informat Engn Yibin 644000 Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Yibin 644000 Peoples R China

To achieve more efficient blind separation of multi-channel speech signals, this paper proposes a new algorithm for blind source separation(BSS) of sound sources using auxiliary function-based independent vector analysis (AuxIVA) with joint pairwise updates of demixing vectors. This algorithm is better than AuxIVA using iterative projection with adjustment (AuxIVA-IPA) when separating multiple sources. The IPA method jointly executes iterative projection (IP) and iterative source steering (ISS) to update and updates one row and one column of the separation matrix in each iteration. On this basis, IPA is extended to jointly execute IP2 and ISS2 for updating, which can update two rows and two columns of the separation matrix in each iteration. Accordingly, this proposed method is named by IPA2. Furthermore, it can optimize the same cost function as IPA while maintaining the same time complexity. Finally, the convolutional speech separation experiments are conducted to validate the effectiveness and efficiency of the proposed method. The experimental results corroborate that compared with the state-of-the-art IP, IP2, ISS, ISS2, and IPA used in AuxIVA, the IPA2 method acquires faster convergence speed and better separation performance, enabling the cost function to reach the convergence interval faster and maintaining good separation results.

关键词： Blind source separation Independent vector analysis auxiliary function optimization Fast algorithm

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FAST AND STABLE BLIND SOURCE SEPARATION WITH RANK-1 UPDATES

FAST AND STABLE BLIND SOURCE SEPARATION WITH RANK-1 UPDATES

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

作者： Scheibler, Robin Ono, Nobutaka Tokyo Metropolitan Univ Tokyo Japan

ISBN: (纸本)9781509066315

We propose a new algorithm for the blind source separation of acoustic sources. This algorithm is an alternative to the popular auxiliary function based independent vector analysis using iterative projection (AuxIVA-IP). It optimizes the same cost function, but instead of alternate updates of the rows of the demixing matrix, we propose a sequence of rank-1 updates. Remarkably, and unlike the previous method, the resulting updates do not require matrix inversion. Moreover, their computational complexity is quadratic in the number of microphones, rather than cubic in AuxIVA-IP. In addition, we show that the new method can be derived as alternate updates of the steering vectors of sources. Accordingly, we name the method iterative source steering (AuxIVA-ISS). Finally, we confirm in simulated experiments that the proposed algorithm separates sources just as well as AuxIVA-IP, at a lower computational cost.

关键词： Blind Source Separation auxiliary function optimization Independent Vector Analysis Mixing Matrix Fast algorithm

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