Classification by multiple-resolution statistical analysis with application to automated recognition of marine mammal...

A multiple-resolution statistical pattern recognition technique for classification by supervised learning is developed and then applied to automated recognition of marine mammal sounds. The data to be classified may be either unprocessed or transformed, e.g., time series or time-frequency distributions of acoustic transients. Training data consist of samples previously grouped by a human expert into labeled sets; these sets are presumed to be associated with different "classes." The labeled sets are then characterized by occupancy statistics associated with a multiple-resolution, binary partition of the (unreduced) sample space. Classification of a new sample is performed by calculating a posteriori probabilities of membership of the new sample in each class, computed by Bayesian inference from the occupancy statistics of the associated labeled set. These a posteriori probabilities are calculated by a recursive algorithm that progresses from coarse to fine resolution in the sample space. The algorithm is implemented in a simple, highly efficient computer program. Automated classification of both time series and time-frequency distributions of marine-mammal vocalizations is demonstrated using a small number of labeled samples (approximately ten samples per class).