Resumen
Passive acoustic monitoring is a method that is commonly used to collect long-term data on soniferous animal presence and abundance. However, these large datasets require substantial effort for manual analysis; therefore, automatic methods are a more effective way to conduct these analyses and extract points of interest. In this study, an energy detector and subsequent pre-trained neural network were used to detect and classify six fish call types from a long-term dataset collected in the northern Gulf of Mexico. The development of this two-step methodology and its performance are the focus of this paper. The energy detector by itself had a high recall rate (>84%), but very low precision; however, a subsequent neural network was used to classify detected signals and remove noise from the detections. Image augmentation and iterative training were used to optimize classification and compensate for the low number of training images for two call types. The classifier had a relatively high average overall accuracy (>87%), but classifier average recall and precision varied greatly for each fish call type (recall: 39?91%; precision: 26?94%). This coupled methodology expedites call extraction and classification and can be applied to other datasets that have multiple, highly variable calls.