Classification of non-stationary random signals using multiple hypotheses testing

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Classification of non-stationary random signals using multiple hypotheses testing

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dc.contributor.author Roberts, G
dc.contributor.author Boashash, B
dc.date.accessioned 2011-09-21T06:32:38Z
dc.date.available 2011-09-21T06:32:38Z
dc.date.issued 1996-06
dc.identifier.citation 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing, 1996., Issue Date : 24-26 Jun 1996 , On page(s): 432 en_US
dc.identifier.isbn 0-8186-7576-4
dc.identifier.uri http://hdl.handle.net/10576/10739
dc.description This paper presents a probabilistic approach to the classification of non-stationary Gaussian signals with multiple hypothesis testing (The most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated). en_US
dc.description.abstract In this paper we introduce a new time-frequency based method for classifying non-stationary random signals. The method involves dividing the signal into overlapping or nonoverlapping segments considered to be subpopulations of the entire population. From each sub-population we calculate a test statistic which can be used to construct a single hypothesis test. To control the global type-I error it is necessary to consider the hypotheses from all subpopulations simultaneously. We use the generalised sequentially rejective Bonferroni multiple hypothesis test which provides an efficient method to simultaneously test multiple hypotheses while maintaining the global type-1 error. Finally, we show the results of classifying time-dependent AR(1) processes which have identical expected instantaneous power and power spectral densities but different time-frequency representations. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Spectrogram en_US
dc.subject Statistical analysis and Testing en_US
dc.subject time-frequency analysis en_US
dc.subject classification en_US
dc.subject multiple hypothesis test en_US
dc.subject global type-I error en_US
dc.subject instantaneous power spectral densities en_US
dc.subject multiple hypotheses testing en_US
dc.subject non-overlapping segments en_US
dc.subject non-stationary random signals en_US
dc.subject overlapping segments en_US
dc.subject power spectral density en_US
dc.subject sub-populations en_US
dc.subject test statistic en_US
dc.subject time-frequency detection en_US
dc.subject time-dependent AR processes en_US
dc.subject time-frequency representations en_US
dc.subject time-frequency distribution en_US
dc.title Classification of non-stationary random signals using multiple hypotheses testing en_US
dc.type Article en_US

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