Fish spawning aggregations usually consist of the gathering of a large number of fishes in high concentration at a specific location. Some large fish species, such as groupers produce sounds during reproductive behaviors. Because aggregation monitoring by divers is often restricted to a limited area, our knowledge of fish spawning aggregation is most likely to be restricted to the surveyed area. In addition, Eulerian passive acoustic monitoring is also limited by the sound propagation range, hence the distance from the fish to the hydrophone. As such, this Eulerian monitoring approach implicitly creates a knowledge gap about what happens beyond the monitoring site. Fisheries independent research strives for new technology that can help remotely and unobtrusively quantify fish biomass. Fish sounds provide an innovative approach to assess fish presence and numbers during reproductive events. However, large datasets make the detection process by a human ear and eyes very tedious and lengthy. We have developed an algorithm based on machine learning and voice recognition methods to identify and classify fish sounds. This algorithm currently operates on a SV3 Liquid Robotics wave glider, an autonomous surface vehicle which has been fitted to accommodate a passive acoustic listening device. Fish sounds detection and classification results, and location along with environmental data are transmitted in real-time enabling verification of the detections with divers or other in-situ methods. Recent surveys in the US Virgin Islands with the SV3 Wave Glider are revealing for the first time the spatial and temporal distribution of fish calls surrounding a known spawning aggregation site. These findings are critical to fish population abundance and stock assessments because calling fish were detected several kilometers away from the main aggregation. These surrounding courtship associated sounds suggest that other spawning aggregations may exist in addition to the main one.