2022/11/14 by Chandra Kanth Nagesh, Nagesh, Chandra Kanth, Abhishek Purushothama +1
Biochemistry, Genetics and Molecular Biology · Environmental Science · #Animal Vocal Communication and Behavior #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Marine animal studies overview #Multimedia (cs.MM) #Sound (cs.SD) #Wildlife Ecology and Conservation #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.07722
openalex publication_date 2022/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Birds are vital parts of ecosystems across the world and are an excellent measure of the quality of life on earth. Many bird species are endangered while others are already extinct. Ecological efforts in understanding and monitoring bird populations are important to conserve their habitat and species, but this mostly relies on manual methods in rough terrains. Recent advances in Machine Learning and Deep Learning have made automatic bird recognition in diverse environments possible. Birdcall recognition till now has been performed using convolutional neural networks. In this work, we try and understand how self-attention can aid in this endeavor. With that we build an pre-trained Attention-based Spectrogram Transformer baseline for BirdCLEF 2022 and compare the results against the pre-trained Convolution-based baseline. Our results show that the transformer models outperformed the convolutional model and we further validate our results by building baselines and analyzing the results for the previous year BirdCLEF 2021 challenge. Source code available at https://github.com/ck090/BirdCLEF-22