23 May 2011 Statistical analysis and classification of acoustic color functions
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In this paper we present a method for clustering and classification of acoustic color data based on statistical analysis of functions using square-root velocity functions (SVRF). The convenience of the SVRF is that it transforms the Fisher-Rao metric into the standard L2 metric. As a result, a formal distance can be calculated using geodesic paths. Moreover, this method allows optimal deformations between acoustic color data to be computed for any two targets allowing for robustness to measurement error. Using the SVRF formulation statistical models can then be constructed using principal component analysis to model the functional variation of acoustic color data. Empirical results demonstrate the utility of functional data analysis for improving performance results in pattern recognition using acoustic color data.
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J. Derek Tucker, J. Derek Tucker, Anuj Srivastava, Anuj Srivastava, "Statistical analysis and classification of acoustic color functions", Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80170O (23 May 2011); doi: 10.1117/12.884537; https://doi.org/10.1117/12.884537

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