Performance Evaluation of Traditional Signal Processing Methods in Localizing Tursiops truncatus Whistles in a Reverberant Aquatic Environment
Woodward, S.; Magnasco, M.
Show abstract
Relative to individually distinctive signature whistles, little is known about the \"non-signature\" calls - particularly the non-signature whistles - of the common Atlantic bottlenose dolphin, Tursiops truncatus. While such calls are suspected to serve social function, tracking their exchange among conspecifics and correlating their usage with non-acoustic behavior has proven challenging, given both their relative scarcity in the dolphin repertoire and their characteristic shared use among dolphins, which precludes the unique identification of callers on the basis of whistle properties alone. Towards the goal of robustly identifying the callers of non-signature whistles (equivalently, attributing non-signature whistles to callers), we present a new, long-term audiovisual monitoring system designed for and tested at the Dolphin Discovery exhibit of the National Aquarium in Baltimore, Maryland. In this paper, we confirm the systems ability to spatially localize impulse-like sounds using traditional signal processing approaches that have already been used to localize dolphin echolocation clicks. We go on to provide the first rigorous experimental evaluation of the component time-difference-of-arrival-(TDOA) extraction methods on whistle-like tonal sounds in a (reverberant) aquatic environment, showing that they are generally not suited to sound localization. Nevertheless, we find that TDOA extraction under these circumstances is performed significantly better using a Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method than a standard circular cross-correlation method, a potentially important result.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Passive acoustic methods for tracking the 3D movements of small cetaceans around marine structures 98%
- Exploring the distribution of statistical feature parameters for natural sound textures 94%
- Automatic detection, identification and counting of anguilliform fish using in situ acoustic camera data: development of a cross-camera morphological analysis approach 94%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.