Machine Learning-based Classification of Birds through Birdsong

• URL: http://arxiv.org/abs/2212.04684v1
• Date: Fri, 9 Dec 2022 06:20:50 GMT
• Title: Machine Learning-based Classification of Birds through Birdsong
• Authors: Yueying Chang and Richard O. Sinnott
• Abstract summary: We apply Mel Frequency Cepstral Coefficients (MFCC) in combination with a range of machine learning models to identify Australian birds. We achieve an overall accuracy of 91% for the top-5 birds from the 30 selected as the case study. Applying the models to more challenging and diverse audio files comprising 152 bird species, we achieve an accuracy of 58%.
• Score: 0.3908842679355254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio sound recognition and classification is used for many tasks and applications including human voice recognition, music recognition and audio tagging. In this paper we apply Mel Frequency Cepstral Coefficients (MFCC) in combination with a range of machine learning models to identify (Australian) birds from publicly available audio files of their birdsong. We present approaches used for data processing and augmentation and compare the results of various state of the art machine learning models. We achieve an overall accuracy of 91% for the top-5 birds from the 30 selected as the case study. Applying the models to more challenging and diverse audio files comprising 152 bird species, we achieve an accuracy of 58%

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