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.

Additional Metadata
Keywords Generalized Likelihood Ratio (GLR), Multi-component signals, Principal components, Segmentation, Singular Value Decomposition (SVD), Wavelet transform
Persistent URL dx.doi.org/10.1117/12.719808
Conference Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Citation
Rajan, S, & Doraiswami, R. (Rajamani). (2007). Singular value decomposition-based segmentation of multi-component signals. In Proceedings of SPIE - The International Society for Optical Engineering. doi:10.1117/12.719808