Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms ...
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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Text |
| Language: | unknown |
| Published: |
arXiv
2023
|
| Subjects: | |
| Online Access: | https://dx.doi.org/10.48550/arxiv.2304.02714 https://arxiv.org/abs/2304.02714 |
| Summary: | 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 ... : Accepted by IEEE Transactions of Multimedia (2023) ... |
|---|