The Birds Need Attention Too: Analysing usage of Self Attention in identifying bird calls in soundscapes
Chandra Kanth Nagesh, Abhishek Purushothama

TL;DR
This paper explores the use of self-attention mechanisms, specifically transformer models, for automatic bird call recognition in soundscapes, demonstrating superior performance over traditional convolutional neural networks.
Contribution
It introduces a pre-trained Attention-based Spectrogram Transformer for bird call identification and compares its effectiveness against convolutional models on BirdCLEF datasets.
Findings
Transformer models outperform convolutional models in bird call recognition
Self-attention mechanisms improve accuracy in diverse soundscape environments
Validated results across BirdCLEF 2021 and 2022 datasets
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnimal Vocal Communication and Behavior · Marine animal studies overview · Wildlife Ecology and Conservation
