Data-efficient classification of birdcall through Convolutional Neural Networks transfer learning
Conference Publication ResearchOnline@JCUAbstract
Deep learning Convolutional Neural Network (CNN) models are powerful classification models but require a large amount of training data. In niche domains such as bird acoustics, it is expensive and difficult to obtain a large number of training samples. One method of classifying data with a limited number of training samples is to employ transfer learning. In this research, we evaluated the effectiveness of birdcall classification using transfer learning from a larger base dataset (2814 samples in 46 classes) to a smaller target dataset (351 samples in 10 classes) using the ResNet-50 CNN. We obtained 79% average validation accuracy on the target dataset in 5-fold cross-validation. The methodology of transfer learning from an ImageNet-trained CNN to a project-specific and a much smaller set of classes and images was extended to the domain of spectrogram images, where the base dataset effectively played the role of the ImageNet.
Journal
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Publication Name
2019 Digital Image Computing: Techniques and Applications (DICTA)
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ISBN/ISSN
978-1-7281-3857-2
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Pages Count
8
Location
Perth, WA, Australia
Publisher
Institute of Electrical and Electronics Engineers
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Publisher Location
Piscataway, NJ, USA
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Date
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DOI
10.1109/DICTA47822.2019.8946016