Time-frequency classification using a multiple hypotheses test: an application to the classification of humpback whale signals

We present a non-stationary signal classification algorithm based on a time-frequency representation and a multiple hypothesis test. The time-frequency representation is used to construct a time-dependent quadratic discriminant function. At selected points in time we evaluate the discriminant function and form a set of statistics which are used to test the multiple hypotheses. The multiple hypotheses are treated simultaneously using the sequentially rejective Bonferroni test to control the probability of incorrect classification of one class. We show results for classifying three classes of humpback whale calls. The results demonstrate that this time-frequency method performs favourably when compared with a frequency domain method which assumes stationarity.