Predictive information in Gaussian processes with application to music analysis

This is the author's accepted manuscript of this article. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40020-9.Lecture Notes in Computer ScienceLecture Notes in Computer ScienceWe describe an information-theoretic approach to the analysis of sequential data, which emphasises the predictive aspects of perception, and the dynamic process of forming and modifying expectations about an unfolding stream of data, characterising these using a set of process information measures. After reviewing the theoretical foundations and the definition of the predictive information rate, we describe how this can be computed for Gaussian processes, including how the approach can be adpated to non-stationary processes, using an online Bayesian spectral estimation method to compute the Bayesian surprise. We finish with a sample analysis of a recording of Steve Reich’s Drummin

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Triennial Report Purpose [Page] 3 Geographical Information Science Center of Excellence [Page] 4 SDSU Faculty [Page] 6 EROS Faculty [Page] 13 Research Professors [Page] 18 Postdoctoral Fellows [Page] 21 GSE Ph.D Program [Page] 30 Ph.D. Students [Page] 31 Ph.D. Fellowships [Page] 44 Recent Ph.D. Graduates [Page] 45 Center Scholars Program and Masters Students [Page] 51 Research Staff [Page] 52 Administrative and Information Technology Staff [Page] 55 Computer Resources [Page] 58 Research Funding [Page] 60 Looking Forward [Page] 61 Appendix I Alumni Faculty and Staff Appendix II Cool Faculty Research and Locations Appendix III Non-Academic Fun Things To Do Appendix IV Publications 2009-2011 Appendix V Directory Appendix VI GIScCE Birthplace Map Appendix VII How To Get To The GIScC

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