Accuracy of Divergence Measures for Detection of Abrupt Changes
Numerous divergence measures (spectral distance, cepstral
distance, difference of the cepstral coefficients, Kullback-Leibler
divergence, distance given by the General Likelihood Ratio, distance
defined by the Recursive Bayesian Changepoint Detector and the
Mahalanobis measure) are compared in this study. The measures are
used for detection of abrupt spectral changes in synthetic AR signals
via the sliding window algorithm. Two experiments are performed;
the first is focused on detection of single boundary while the second
concentrates on detection of a couple of boundaries. Accuracy of
detection is judged for each method; the measures are compared
according to results of both experiments.
[1] U. Appel, "A comparative study of three sequential time series segmentation
algorithms," Signal Processing, vol. 6,pp. 45 − 60, 1984.
[2] U. Appel, "Adaptive Segmentation of Piecewise Stationary Time Series,"
Information Sciences, no.29,pp. 27 − 56, 1983.
[3] J.O. Ruanaidh, W.J. Fitzgerald, Numerical Bayesian Methods Applied
to Signal Processing, Statistics and Computing, Springer, Berlin, 1996.
[4] R. Cmejla, P. Sovka, "Recursive Bayesian Autoregressive Changepoint
Detector for Sequential Signal Segmentation," in EUSIPCO-2004 -
Proceedings [CD-ROM]. Wien: Technische Universitat, 2004.
[5] J.J. Sooful, J.C. Botha, "An acoustic distance measure for automatic
cross-language phoneme mapping," PRASA-01, pp. 99 − 102, South
Africa, November 2001.
[6] L. Couvreur, J.M. Boite, "Speaker Tracking in Broadcast Audio Material
in the Framework of the THISL Project," Proc. of ESCA ETRW
Workshop on Accessing Information in Spoken Audio, Cambridge (UK),
pp. 84 − 89, April 1999.
[1] U. Appel, "A comparative study of three sequential time series segmentation
algorithms," Signal Processing, vol. 6,pp. 45 − 60, 1984.
[2] U. Appel, "Adaptive Segmentation of Piecewise Stationary Time Series,"
Information Sciences, no.29,pp. 27 − 56, 1983.
[3] J.O. Ruanaidh, W.J. Fitzgerald, Numerical Bayesian Methods Applied
to Signal Processing, Statistics and Computing, Springer, Berlin, 1996.
[4] R. Cmejla, P. Sovka, "Recursive Bayesian Autoregressive Changepoint
Detector for Sequential Signal Segmentation," in EUSIPCO-2004 -
Proceedings [CD-ROM]. Wien: Technische Universitat, 2004.
[5] J.J. Sooful, J.C. Botha, "An acoustic distance measure for automatic
cross-language phoneme mapping," PRASA-01, pp. 99 − 102, South
Africa, November 2001.
[6] L. Couvreur, J.M. Boite, "Speaker Tracking in Broadcast Audio Material
in the Framework of the THISL Project," Proc. of ESCA ETRW
Workshop on Accessing Information in Spoken Audio, Cambridge (UK),
pp. 84 − 89, April 1999.
@article{"International Journal of Medical, Medicine and Health Sciences:50867", author = "P. Bergl", title = "Accuracy of Divergence Measures for Detection of Abrupt Changes", abstract = "Numerous divergence measures (spectral distance, cepstral
distance, difference of the cepstral coefficients, Kullback-Leibler
divergence, distance given by the General Likelihood Ratio, distance
defined by the Recursive Bayesian Changepoint Detector and the
Mahalanobis measure) are compared in this study. The measures are
used for detection of abrupt spectral changes in synthetic AR signals
via the sliding window algorithm. Two experiments are performed;
the first is focused on detection of single boundary while the second
concentrates on detection of a couple of boundaries. Accuracy of
detection is judged for each method; the measures are compared
according to results of both experiments.", keywords = "Abrupt changes detection, autoregressive model, divergence measure.", volume = "2", number = "6", pages = "191-4", }