Method for passive acoustic monitoring of bird communities using UMAP and a deep neural network

Abstract

An effective practice for monitoring bird communities is the recognition and identification of their acoustic signals, whether simple, complex, fixed or variable. A method for the passive monitoring of diversity, activity and acoustic phenology of structural species of a bird community in an annual cycle is presented. The method includes the semi-automatic elaboration of a dataset of 22 vocal and instrumental forms of 16 species. To analyze bioacoustic richness, the UMAP algorithm was run on two parallel feature extraction channels. A convolutional neural network was trained using STFT-Mel spectrograms to perform the task of automatic identification of bird species. The predictive performance was evaluated by obtaining a minimum average precision of 0.79, a maximum equal to 1.0 and a mAP equal to 0.97. The model was applied to a huge set of passive recordings made in a network of urban wetlands for one year. The acoustic activity results were synchronized with climatological temperature data and sunlight hours. The results confirm that the proposed method allows for monitoring a taxonomically diverse group of birds that nourish the annual soundscape of an ecosystem, as well as detecting the presence of cryptic species that often go unnoticed.