16422

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Balint Pal Toth, Balint Czeba
Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Magyar Tudosok krt. 2., H-1117, Budapest, Hungary
Conference and Labs of the Evaluation forum (CLEF), 2016
@article{toth2016convolutional,

   title={Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment},

   author={T{‘o}th, B{‘a}lint P{‘a}l and Czeba, B{‘a}lint},

   year={2016}

}

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This paper describes a convolutional neural network based deep learning approach for bird song classification that was used in an audio record-based bird identification challenge, called BirdCLEF 2016. The training and test set contained about 24k and 8.5k recordings, belonging to 999 bird species. The recorded waveforms were very diverse in terms of length and content. We converted the waveforms into frequency domain and splitted into equal segments. The segments were fed into a convolutional neural network for feature learning, which was followed by fully connected layers for classification. In the official scores our solution reached a MAP score of over 40% for main species, and MAP score of over 33% for main species mixed with background species.
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Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment, 3.3 out of 5 based on 4 ratings

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