Improve automatic detection of animal call sequences with temporal context
Abstract
Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale ( Balaenoptera physalus ) songs, which comprise patterned sequences of characteristic notes. We evaluated several variants of the CNN + LSTM network. Relative to the baseline CNN model, the CNN + LSTM models reduced performance variance, offering a 9–17% increase in area under the precision–recall curve and a 9–18% increase in peak F1-scores. These results show that the inclusion of temporal information may offer a valuable pathway for improving the automatic recognition and transcription of wildlife recordings.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 01, 2021
- Source ID
- 10.1098/rsif.2021.0297
Entities
People
- Ana Sirovic
- Danielle Cholewiak
- Douglas Gillespie
- Erica Fleishman
- Eva‐Marie Nosal
- Holger Klinck
- Marie A. Roch
- Shyam Madhusudhana
- Tyler A Helble
- Xiaobai Liu
- Yu Shiu
Organizations
- Cornell University
- National Marine Fisheries Service
- Office of Naval Research
- Oregon State University
- San Diego State University
- United States Navy
- University of Hawaiʻi at Mānoa
- University of St Andrews