Methods for processing and analyzing passive acoustic monitoring data: An example of song recognition in western black-crested gibbons

Abstract

The extraction of species-specific calls from passive acoustic recordings is a common preliminary step in ecological analysis. But for many species, especially those occupying noisy, acoustically variable habitats, the call extraction process remains largely manual, a time-consuming, and increasingly unsustainable process. Deep neural networks have been shown to provide excellent performance in a range of acoustic classification applications. We take as an example the recognition of four songs of one of the rarest mammals in the world, the western black-crested gibbon (Nomascus concolors).The process of recognition in this paper which includes distributed BIC ambient sound segmentation based on the OpenPAI platform; DNN-based western black-crested gibbon song enhancement processing; data pre-processing, labeling samples; proposed DNN + ResNet34 + CBAM + GRU + Attention recognition model; comparing other classical neural network models.Our best model converts acoustic recordings into spectrogram images on the mel frequency scale and uses these images to train convolutional neural networks.Our proposed model proved to be very accurate in predicting the segmented western black-crested gibbon songs with an accuracy of 99.8%, and almost a few western black-crested gibbon songs were incorrectly identified when all segmented data were recognized. In the four consecutive years of the acoustic monitoring system deployed in the Ailao Mountain National Nature Reserve, the western black-crested gibbon was most active near the monitoring site from March to August each year, and least active in January and February. Based on call sound intensity analysis, we monitored a total of two different western black-crested gibbon groups (G1 and G2) during the monitoring cycle. We demonstrate that passive acoustic monitoring combined with CNN classifiers is an effective tool for the remote detection of one of the rarest and most threatened species in the world.