25 August 2006 Time-frequency decomposition based on information
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Proceedings Volume 6313, Advanced Signal Processing Algorithms, Architectures, and Implementations XVI; 63130R (2006); doi: 10.1117/12.680918
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States
Abstract
In array processing applications, it is desirable to extract the sources that generate the observed signals. There are various source separation and component extraction algorithms in literature including principal component analysis (PCA) and independent component analysis (ICA). However, most of these methods are not designed to deal with time-varying signals and thus are formulated in the time domain. In this paper, we introduce a new time-frequency based decomposition method using an information measure as the decomposition criteria. It is shown that under the assumption of disjoint source signals on the time-frequency plane, this method can extract the sources up to a scalar factor. Based on the QR decomposition of the mixing matrix, the source extraction algorithm is reduced to finding the optimal N-dimensional rotation of the observed time-frequency distributions. The proposed algorithm is implemented using the steepest descent approach to find the optimal rotation angle. The performance of the method is illustrated for example signals and compared to some well-known decomposition techniques.
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Selin Aviyente, "Time-frequency decomposition based on information", Proc. SPIE 6313, Advanced Signal Processing Algorithms, Architectures, and Implementations XVI, 63130R (25 August 2006); doi: 10.1117/12.680918; https://doi.org/10.1117/12.680918
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KEYWORDS
Time-frequency analysis

Independent component analysis

Transform theory

Principal component analysis

Associative arrays

Image information entropy

Matrices

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