Abstract
The number of sources in an underdetermined blind source separation (UBSS) is more than the observed mixed signals. The UBSS involves two stages. In the first stage, the mixing matrix is estimated, and in the second, the source separation is performd. Researchers have proposed a number of methods based on clustering, time-frequency, and sparse component analysis to address UBSS. The performance in source separation as well as the estimated mixing matrix coefficients utilising older methods differed from the actual mixing matrix. In this approach, source signals are recovered by using the series of least square problem which is known as modified sparse component analysis. The suggested method estimates the mixing matrix by considering the one dimensional subspace, associated with time-frequency points of mixtures combined with hierarchical clustering. The one dimensional subspace is considered only for active source in which more energy exists compared to other sources in the mixing matrix estimation process. The proposed method’s performance is contrasted with that of approaches created using single source points. According on experimental findings, the suggested methodology performs better than other common approaches.
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Vaghmare, A.K. (2023). BSS in Underdetermined Applications Using Modified Sparse Component Analysis. In: Sharma, H., Saha, A.K., Prasad, M. (eds) Proceedings of International Conference on Intelligent Vision and Computing (ICIVC 2022). ICIVC 2022. Proceedings in Adaptation, Learning and Optimization, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-031-31164-2_31
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