  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.
