Automated passive acoustic monitoring with machine learning
AssigneeBLUEiQ LLC
InventorsKimberly E Gavin, Robert Joseph McGurrin
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AssigneeBLUEiQ LLC
InventorsKimberly E Gavin, Robert Joseph McGurrin
Estimating the term…
Disclosed embodiments provide techniques for automated passive acoustic monitoring with machine learning. An acoustic sensor is accessed. The acoustic sensor includes an embedded acoustic controller which hosts a machine learning model. The acoustic sensor is coupled to one or more hydrophones. The acoustic sensor is deployed in a body of water and is submerged. The acoustic sensor can enter a sleep mode. The hydrophones receive an underwater audio signal. The audio signal can be associated with an acoustic pressure. The acoustic sensor can be woken from sleep when the acoustic pressure is above a pressure threshold. The machine learning model classifies a predicted source of the underwater audio signal. The classifying can be based on filtering the underwater audio signal for a first frequency band associated with a source of interest. The predicted source is reported to a user using a communications device.
A processor-implemented method for monitoring acoustic data comprising:
A computer program product embodied in a non-transitory computer readable medium for monitoring acoustic data, the computer program product comprising code which causes one or more processors to perform operations of:
A computer system for monitoring acoustic data, comprising:
Dependencies are inferred from the claim text.