Singular value decomposition-based segmentation of multi-component signals

Sreeraman Rajan; Rajamani Doraiswami

doi:10.1117/12.719808

9 April 2007 Singular value decomposition-based segmentation of multi-component signals

Sreeraman Rajan, Rajamani Doraiswami

Proceedings Volume 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V; 657609 (2007) https://doi.org/10.1117/12.719808
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

A methodology for segmentation of multi-component signals buried in additive white Gaussian noise using singular value decomposition (SVD) in the time-frequency domain is proposed. The segmentation problem is posed as a binary statistical hypothesis testing problem. Using the Generalized Likelihood Ratio (GLR), the optimal test statistic is shown to be the sum of squares of the norms of the principal components of the signal in the time-frequency domain. The signal-to-noise ratio (SNR) at the dominant signal frequencies is assumed to be sufficiently high to determine the bandwidth of the signal components. The proposed segmentation methodology is evaluated on phonocardiogram (PCG) signals.

Citation Download Citation

Sreeraman Rajan and Rajamani Doraiswami "Singular value decomposition-based segmentation of multi-component signals", Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 657609 (9 April 2007); https://doi.org/10.1117/12.719808

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