An Efficient Model for a Vast Number of Bird Species Identification Based on Acoustic Features

SIMPLE SUMMARY: Identifying bird species is very important in bird biodiversity surveys. Bird vocalizations can be utilized to identify bird species. In this paper, we utilized massive amounts of data of bird calls and proposed a novel, efficient model for identifying bird species based on acoustic features. A novel method was proposed for audio preprocessing and attention mechanism embedding. Our proposed model achieved improved performance in identifying a larger number of bird species. Our work might be useful for bird species identification and avian biodiversity monitoring. ABSTRACT: Birds have been widely considered crucial indicators of biodiversity. It is essential to identify bird species precisely for biodiversity surveys. With the rapid development of artificial intelligence, bird species identification has been facilitated by deep learning using audio samples. Prior studies mainly focused on identifying several bird species using deep learning or machine learning based on acoustic features. In this paper, we proposed a novel deep learning method to better identify a large number of bird species based on their call. The proposed method was made of LSTM (Long Short−Term Memory) with coordinate attention. More than 70,000 bird−call audio clips, including 264 bird species, were collected from Xeno−Canto. An evaluation experiment showed that our proposed network achieved 77.43% mean average precision (mAP), which indicates that our proposed network is valuable for automatically identifying a massive number of bird species based on acoustic features and avian biodiversity monitoring.