Few-shot Long-Tailed Bird Audio Recognition

• URL: http://arxiv.org/abs/2206.11260v1
• Date: Wed, 22 Jun 2022 04:14:25 GMT
• Title: Few-shot Long-Tailed Bird Audio Recognition
• Authors: Marcos V. Conde and Ui-Jin Choi
• Abstract summary: We propose a sound detection and classification pipeline to analyze soundscape recordings. Our solution achieved 18th place of 807 teams at the BirdCLEF 2022 Challenge hosted on Kaggle.
• Score: 3.8073142980733
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: It is easier to hear birds than see them. However, they still play an essential role in nature and are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Machine Learning and Convolutional Neural Networks allow us to process continuous audio data to detect and classify bird sounds. This technology can assist researchers in monitoring bird populations' status and trends and ecosystems' biodiversity. We propose a sound detection and classification pipeline to analyze complex soundscape recordings and identify birdcalls in the background. Our method learns from weak labels and few data and acoustically recognizes the bird species. Our solution achieved 18th place of 807 teams at the BirdCLEF 2022 Challenge hosted on Kaggle.

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