ORCID iD
Anis: 0009-0003-3739-9166
Kellis: 0000-0002-1520-0238
Wilson: 0000-0003-4770-9669
O'Reilly: 0000-0002-3149-4934
Document Type
Paper
Subject Area(s)
Ultrasonic vocalization, signal processing
Abstract
Analyzing ultrasonic vocalizations (USVs) is crucial for understanding rodents' affective states and social behaviors, but the manual analysis is time-consuming and prone to errors. Automated USV detection systems have been developed to address these challenges. Yet, these systems often rely on machine learning and fail to generalize effectively to new datasets. To tackle these shortcomings, we introduce ContourUSV, an efficient automated system for detecting USVs from audio recordings. Our pipeline includes spectrogram generation, cleaning, pre-processing, contour detection, post-processing, and evaluation against manual annotations. To ensure robustness and reliability, we compared ContourUSV with three state-of-the-art systems using an existing open-access USV dataset (USVSEG) and a second dataset we are releasing publicly along with this paper. On average, across the two datasets, ContourUSV outperformed the other three systems with a 1.51× improvement in precision, 1.17× in recall, 1.80× in F1 score, and 1.49× in specificity while achieving an average speedup of 117.07×.
Digital Object Identifier (DOI)
Publication Info
Published in 7th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2025), ed. Sergey Y. Yurish, Spring 2025, pages 72-78.
Rights
© 2025, International Frequency Sensor Association (IFSA) Publishing, S. L.
Reposted with permission.
APA Citation
Anis, S. S., Kellis, D. M., Kaigler, K. F., Wilson, M. A., & O'Reilly, C. (2025). A reliable and efficient detection pipeline for rodent ultrasonic vocalizations. Proceedings of the 7th International Conference on Advances in Signal Processing and Artificial Intelligence. http://dx.doi.org/10.13140/RG.2.2.14725.46566