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Free keywords:
Killer Whale; Denoising; Call Type; Deep Learning; Orca
Abstract:
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
