Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Conference Paper

ORCA-CLEAN: A deep denoising toolkit for killer whale communication


Smeele,  S.       
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

External Resource
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Bergler, C., Schmitt, M., Maier, A., Smeele, S., Barth, V., & Noth, E. (2020). ORCA-CLEAN: A deep denoising toolkit for killer whale communication. In Interspeech 2020 (pp. 1136-1140). International Speech Communication Association.

Cite as: https://hdl.handle.net/21.11116/0000-0007-FE01-A
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