Groupers

This research presents findings from an in-situ experiment utilizing a hydrophone line array to capture the sound production of the Goliath grouper. Analysis revealed that Goliath grouper calls exhibit multiple frequency components, including one high-amplitude component and 2 to 3 low-amplitude components. The primary high-amplitude component is concentrated in the 30 to 70 Hz band, peaking around 50 Hz, while low-amplitude components span 20 to 30 Hz, 70 to 115 Hz, and 130 to 200 Hz. Comparison between in-situ data and results from a normal modes transmission loss model identified regions where echo level increased with propagation distance. This suggests that the loudness of the call may not necessarily indicate proximity, indicating the Goliath grouper might rely on other cues for localization, such as changes in the frequency profile of its call. Two methods for estimating call distance are presented. The first method vi utilized a transmission loss model and measured transmission loss across a hydrophone line array. This method could also determine the source level of the calls, yielding source level estimates ranging from 124.01 to 144.83 dB re 1 μPa. The second method employed match field filtering, validating the accuracy of the transmission loss model. Both methods produced similar call distance estimations, ranging from 11.5 to 17.1 meters, placing the grouper inside or near its typical habitat.

Deep learning models have been successfully applied to a variety of machine learning tasks, including image identification, image segmentation, object detection, speaker recognition, natural language processing, bioinformatics and drug discovery, among other things. This dissertation introduces Multi-Model Deep Learning (MMDL), a new ensemble deep learning approach for signal classification and event forecasting. The ultimate goal of the MMDL method is to improve classification and forecasting performances of individual classifiers by fusing results of participating deep learning models. The performance of such an ensemble model, however, depends heavily on the following two design features. Firstly, the diversity of the participating (or base) deep learning models is crucial. If all base deep learning models produce similar classification results, then combining these results will not provide much improvement. Thus, diversity is considered to be a key design feature of any successful MMDL system. Secondly, the selection of a fusion function, namely, a suitable function to integrate the results of all the base models, is important. In short, building an effective MMDL system is a complex and challenging process which requires deep knowledge of the problem context and a well-defined prediction process. The proposed MMDL method utilizes a bank of Convolutional Neural Networks (CNNs) and Stacked AutoEncoders (SAEs). To reduce the design complexity, a randomized generation process is applied to assign values to hyperparameters of base models. To speed up the training process, new feature extraction procedures which captures time-spatial characteristics of input signals are also explored. The effectiveness of the MMDL method is validated in this dissertation study with three real-world case studies. In the first case study, the MMDL model is applied to classify call types of groupers, an important fishery resource in the Caribbean that produces sounds associated with reproductive behaviors during yearly spawning aggregations. In the second case study, the MMDL model is applied to detect upcalls of North Atlantic Right Whales (NARWs), a type of endangered whales. NARWs use upcalls to communicate among themselves. In the third case study, the MMDL model is modified to predict seizure episodes. In all these cases, the proposed MMDL model outperforms existing state-of-the-art methods.