Deep Learning for Orca Call Type Identification – A Fully Unsupervised Approach - Bergler, Christian; Schmitt, Manuel; Cheng, Rachael Xi; Maier, Andreas; Barth, Volker; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
Call type classification is an important instrument in bioacoustic investigating group-specific vocal repertoire, behavioral patterns, and cultures of different animal groups. There is a need using robust machine-based techniques to replace classification due to its advantages in handling large delivering consistent results, removing perceptual-based classification, and minimizing human errors. The current work is the first adopting a two-stage fully unsupervised approach on previous machine-segmented orca data to identify orca sound types using deep learning together with one of the largest bioacoustic datasets – the Orchive. The proposed methods include: (1) unsupervised feature learning using an undercomplete ResNet18-autoencoder trained on machine-annotated data, and (2) spectral clustering utilizing compressed orca feature representations. An existing human-labeled orca dataset was clustered, including 514 signals distributed over 12 classes. This two-stage fully unsupervised approach is an initial study to (1) examine machine-generated clusters against human-identified orca call type classes, (2) compare supervised call type classification versus unsupervised call type clustering, and (3) verify the general feasibility of a completely unsupervised approach based on machine-labeled orca data resulting in a major progress within the research field of animal linguistics, by deriving a much deeper understanding and facilitating totally new insights and opportunities.
CRIS-Team Scopus Importer:2019-11-19
Keywords: orca; call type; unsupervised; deep learning; clustering

ORCA-PARTY: An Automatic Killer Whale Sound Type Separation Toolkit Using Deep Learning - Bergler, Christian; Schmitt, Manuel; Maier, Andreas; Cheng, Rachael Xi; Barth, Volker; Nöth, Elmar (ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022)
Data-driven and machine-based analysis of massive bioacoustic data collections, in particular acoustic regions containing a substantial number of vocalizations events, is essential and extremely valuable to identify recurring vocal paradigms. However, these acoustic sections are usually characterized by a strong incidence of overlapping vocalization events, a major problem severely affecting subsequent human-/machine- based analysis and interpretation. Robust machine-driven signal separation of species-specific call types is extremely challenging due to missing ground truth data, speaker/source- relevant information, limited knowledge about inter- and intra-call type variations, next to diverse recording conditions. The current study is the first introducing a fully- automated deep signal separation approach for overlapping orca vocalizations, addressing all of the previously mentioned challenges, together with one of the largest bioacoustic data archives recorded on killer whales (Orcinus Orca). Incorporating ORCA-PARTY as additional data enhancement step for downstream call type classification demonstrated to be extremely valuable. Besides the proof of cross-domain applicability and consistently promising results on non-overlapping signals, significant improvements were achieved when processing acoustic orca segments comprising a multitude of vocal activities. Apart from auspicious visual inspections, a final numerical evaluation on an unseen dataset proved that about 30 % more known sound patterns could be identified.<br
Keywords: Killer Whale; Deep Learning; Call Type