  Whistle contour extraction aims to derive animal whistles from time-frequency
spectrograms as polylines. For toothed whales, whistle extraction results can
serve as the basis for analyzing animal abundance, species identity, and social
activities. During the last few decades, as long-term recording systems have
become affordable, automated whistle extraction algorithms were proposed to
process large volumes of recording data. Recently, a deep learning-based method
demonstrated superior performance in extracting whistles under varying noise
conditions. However, training such networks requires a large amount of
labor-intensive annotation, which is not available for many species. To
overcome this limitation, we present a framework of stage-wise generative
adversarial networks (GANs), which compile new whistle data suitable for deep
model training via three stages: generation of background noise in the
spectrogram, generation of whistle contours, and generation of whistle signals.
By separating the generation of different components in the samples, our
framework composes visually promising whistle data and labels even when few
expert annotated data are available. Regardless of the amount of
human-annotated data, the proposed data augmentation framework leads to a
consistent improvement in performance of the whistle extraction model, with a
maximum increase of 1.69 in the whistle extraction mean F1-score. Our
stage-wise GAN also surpasses one single GAN in improving whistle extraction
models with augmented data. The data and code will be available at
https://github.com/Paul-LiPu/CompositeGAN\_WhistleAugment.
