RECURSIVE BAYESIAN AUTOREGRESSIVE CHANGEPOINT DETECTOR FOR SEQUENTIAL SIGNAL SEGMENTATION (TuePmOR1)

Author(s) : Roman Cmejla (Czech Technical University in Prague, Czech Republic) Pavel Sovka (Czech Technical University in Prague, Czech Republic)

Abstract : The contribution addresses a sliding window modification of the Bayesian autoregressive change-point detector (BACD) enabling the sequential localization of signal changes (change-point detection). The modification consists in using the simplified data-dependent Bayesian evidence normaliz-ing the classical BACD formula and in the recursive evalua-tion of these two functions. The suggested approach seems to be computationally effective and numerical stable as shown by experiments. Apart from the evaluation of the algorithm accuracy two illustrative examples with modelled signals are given. One application to the violin signal seg-mentation demonstrates the algorithm performance – even relatively weak and gradual signal changes can be detected.

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