Reference number : GC-2017-Q4-01194, amendment number : , agreement type : G, recipient legal name : Matwin, Stan (Dalhousie University), recipient country : CA, recipient province : nan, recipient city : Nova Scotia|Nouvelle-Écosse, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : JASCO is a leading provider of passive acoustic monitoring solutions to the marine construction and petroleum_x000D_
exploration industries from our headquarters in Halifax, NS. JASCO regularly partners with government and_x000D_
academia on soundscape and marine mammal habitat research projects. As part of these projects JASCO_x000D_
collects continuous acoustic datasets that last from a day to year for multiple recorders over wide areas. To date_x000D_
JASCO accumulated 600,000 'truth-data' annotations from hundreds of types of marine mammal species and_x000D_
noise sources. JASCO has identified a need to improve their marine mammal detection and classification_x000D_
algorithms. Current detector/classifiers are effective, but the expectation is that new machine learning_x000D_
techniques will both improve the probability of detection and will reduce false alarms. Being able to provide_x000D_
reliable results quickly to universities, government and industry is a unique offering that JASCO believes it_x000D_
must continually improve to maintain it's market leader position. While JASCO has rich and extensive internal_x000D_
expertise in ocean acoustic data, it will benefit from a pilot study on the use of state-of-the-art Machine_x000D_
learning expertise in classifier development, contributed by Dalhousie University. We will apply several_x000D_
Machine Learning classifiers on the labeled data. Moreover, we will experiment with data re-representation_x000D_
techniques known to perform well-from a Deep Learning perspective - when the representation is directly_x000D_
learned from the data used for a given task (here, species classification). Convolutional Neural Networks_x000D_
(CNNs) are a promising approach. The hypothesis is that CNNs can produce a compact but at the same time_x000D_
highly performing representation to be used by one of the machine learning classifiers mentioned above._x000D_
Finally, it is likely that due to the scale of the data, efficiency problems may arise wrt at least some of the_x000D_
methods (eg clustering). If that will be the case, we will look into the use of GPU-based solutions either thru_x000D_
the dedicated proprietary resources, or on Compute Canada's facilities with multiple GPU availability., program purpose french : JASCO is a leading provider of passive acoustic monitoring solutions to the marine construction and petroleum_x000D_
exploration industries from our headquarters in Halifax, NS. JASCO regularly partners with government and_x000D_
academia on soundscape and marine mammal habitat research projects. As part of these projects JASCO_x000D_
collects continuous acoustic datasets that last from a day to year for multiple recorders over wide areas. To date_x000D_
JASCO accumulated 600,000 'truth-data' annotations from hundreds of types of marine mammal species and_x000D_
noise sources. JASCO has identified a need to improve their marine mammal detection and classification_x000D_
algorithms. Current detector/classifiers are effective, but the expectation is that new machine learning_x000D_
techniques will both improve the probability of detection and will reduce false alarms. Being able to provide_x000D_
reliable results quickly to universities, government and industry is a unique offering that JASCO believes it_x000D_
must continually improve to maintain it's market leader position. While JASCO has rich and extensive internal_x000D_
expertise in ocean acoustic data, it will benefit from a pilot study on the use of state-of-the-art Machine_x000D_
learning expertise in classifier development, contributed by Dalhousie University. We will apply several_x000D_
Machine Learning classifiers on the labeled data. Moreover, we will experiment with data re-representation_x000D_
techniques known to perform well-from a Deep Learning perspective - when the representation is directly_x000D_
learned from the data used for a given task (here, species classification). Convolutional Neural Networks_x000D_
(CNNs) are a promising approach. The hypothesis is that CNNs can produce a compact but at the same time_x000D_
highly performing representation to be used by one of the machine learning classifiers mentioned above._x000D_
Finally, it is likely that due to the scale of the data, efficiency problems may arise wrt at least some of the_x000D_
methods (eg clustering). If that will be the case, we will look into the use of GPU-based solutions either thru_x000D_
the dedicated proprietary resources, or on Compute Canada's facilities with multiple GPU availability., agreement title english : State of The Art Machine Learning Methods For Classification of Animals In Ocean Acoustics Data, agreement title french : State of The Art Machine Learning Methods For Classification of Animals In Ocean Acoustics Data, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-22, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada