Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer Learning
| Autor: | Dina B. Efremova, Mangalam Sankupellay, Dmitry A. Konovalov |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0106 biological sciences Sound (cs.SD) Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010603 evolutionary biology 01 natural sciences Convolutional neural network Computer Science - Sound Domain (software engineering) Set (abstract data type) Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Training set business.industry Deep learning Image and Video Processing (eess.IV) Pattern recognition Electrical Engineering and Systems Science - Image and Video Processing Base (topology) Multimedia (cs.MM) Spectrogram 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business Computer Science - Multimedia Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | DICTA |
| Popis: | 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. Accepted for IEEE Digital Image Computing: Techniques and Applications, 2019 (DICTA 2019), 2-4 December 2019 in Perth, Australia, http://dicta2019.dictaconference.org/index.html |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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