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

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. •Monitoring bird communities,•UMAP algorithm,•Convolutional neural networks