Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer Learning

Dina B. Efremova; Mangalam Sankupellay; Dmitry A. Konovalov

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arXiv · 1909.07526

Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer Learning

Dina B. Efremova·Mangalam Sankupellay·Dmitry A. Konovalov

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Abstract

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.

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BibTeX RIS

Dina B. Efremova, Mangalam Sankupellay, Dmitry A. Konovalov. 2019-09-17. Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer Learning. https://arxiv.org/abs/1909.07526

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