Classification of red hind grouper call types using random ensemble of stacked autoencoders

In this paper, a method is introduced for the classification of call types of red hind grouper, an important fishery resource in the Caribbean that produces sounds associated with reproductive behaviors during yearly spawning aggregations. For the undertaken task, two distinct call types of red hind are analyzed. An ensemble of stacked autoencoders (SAEs) is then designed by randomly selecting the hyperparameters of SAEs in the network. These hyperparameters include a number of hidden layers in each SAE and a number of nodes in each hidden layer. Spectrograms of red hind calls are used to train this randomly generated ensemble of SAEs one at a time. Once all individual SAEs are trained, this ensemble is used as a whole to classify call types of red hind. More specifically, the outputs of individual SAEs are combined with a fusion mechanism to produce a final decision on the call type of the input red hind sound. Experimental results show that the innovative approach produces superior results in comparison with those obtained by non-ensemble methods. The algorithm reliably classified red hind call types with over 90% accuracy and successfully detected some calls missed by human observers.

An approach for automatic classification of grouper vocalizations with passive acoustic monitoring

Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50-350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classification of grouper vocalizations from ambient sounds recorded in situ with fixed hydrophones based on weighted features and sparse classifier. Group sounds were labeled initially by humans for training and testing various feature extraction and classification methods. In the feature extraction phase, four types of features were used to extract features of sounds produced by groupers. Once the sound features were extracted, three types of representative classifiers were applied to categorize the species that produced these sounds. Experimental results showed that the overall percentage of identification using the best combination of the selected feature extractor weighted mel frequency cepstral coefficients and sparse classifier achieved 82.7% accuracy. The proposed algorithm has been implemented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.