State of The Art Machine Learning Methods For Classification of Animals In Ocean Acoustics Data

JASCO is a leading provider of passive acoustic monitoring solutions to the marine construction and petroleumexploration industries from our headquarters in Halifax, NS. JASCO regularly partners with government andacademia on soundscape and marine mammal habitat research projects. As part of these projects JASCOcollects continuous acoustic datasets that last from a day to year for multiple recorders over wide areas. To dateJASCO accumulated 600,000 'truth-data' annotations from hundreds of types of marine mammal species andnoise sources. JASCO has identified a need to improve their marine mammal detection and classificationalgorithms. Current detector/classifiers are effective, but the expectation is that new machine learningtechniques will both improve the probability of detection and will reduce false alarms. Being able to providereliable results quickly to universities, government and industry is a unique offering that JASCO believes itmust continually improve to maintain it's market leader position. While JASCO has rich and extensive internalexpertise in ocean acoustic data, it will benefit from a pilot study on the use of state-of-the-art Machinelearning expertise in classifier development, contributed by Dalhousie University. We will apply severalMachine Learning classifiers on the labeled data. Moreover, we will experiment with data re-representationtechniques known to perform well-from a Deep Learning perspective - when the representation is directlylearned from the data used for a given task (here, species classification). Convolutional Neural Networks(CNNs) are a promising approach. The hypothesis is that CNNs can produce a compact but at the same timehighly performing representation to be used by one of the machine learning classifiers mentioned above.Finally, it is likely that due to the scale of the data, efficiency problems may arise wrt at least some of themethods (eg clustering). If that will be the case, we will look into the use of GPU-based solutions either thruthe dedicated proprietary resources, or on Compute Canada's facilities with multiple GPU availability.