| Summary: | In bioacoustics, passive acoustic monitoring of animals living in the wild, both on land and underwater, leads to large data archives characterized by a strong imbalance between recorded animal sounds and ambient noises. Bioacoustic datasets suffer extremely from such large noise-variety, caused by a multitude of external influences and changing environmental conditions over years. This leads to significant deficiencies/problems concerning the analysis and interpretation of animal vocalizations by biologists and machine-learning algorithms. To counteract such huge noise diversity, it is essential to develop a denoising procedure enabling automated, efficient, and robust data enhancement. However, a fundamental problem is the lack of clean/denoised ground-truth samples. The current work is the first presenting a fully-automated deep denoising approach for bioacoustics, not requiring any clean ground-truth, together with one of the largest data archives recorded on killer whales (Orcinus Orca) – the Orchive. Therefor, an approach, originally developed for image restoration, known as Noise2Noise (N2N), was transferred to the field of bioacoustics, and extended by using automatic machine-generated binary masks as additional network attention mechanism. Besides a significant cross-domain signal enhancement, our previous results regarding supervised orca/noise segmentation and orca call type identification were outperformed by applying ORCACLEAN as additional data preprocessing/enhancement step
|
|---|