Passive Acoustic Monitoring of Wolves

1. Passive Acoustic Monitoring of Wolves Deborah Curless, Marie A. Roch, Shyam Kumar Madhusudhana

2. Project Collaborators Dan Moriarty, Elizabeth Baker Kim Miller, Melinda Booth John A. Hildebrand, Melissa S. Soldevilla We would like to thank the HPWREN project and the NSF for their support of this work through the NSF grant 0426879.

3. Project Overview • Audio data from California Wolf Center • Interested in wolf vocalizations • Statistical pattern recognition techniques to characterize these vocalizations

4. Data Acquisition SDSU Speech Processing Lab California Wolf Center HPWREN • Audio Recording • SNR Endpoint Detector • Transmit Detected Segments via HPWREN

5. Possible Applications • Classify types of vocalizations • Species identification • Individual identification • Effects of anthropomorphic sounds • Possible use in care of wolves

6. Call Classification • Data labeling • ethogram descriptions used as basis for labels • Feature extraction • reduces data set and enhances distinguishability • Training data versus test data • Hidden Markov models

7. Call Classification hidden Markov models SDSU Speech Processing Lab howl decision logic whine howl detected signal bark

8. Current Status • Labeled corpus is approximately 4 hours • Performance is approximately 65% over all classes • How data is divided • training data: 70% test data: 30% • rotate specific training data and test data

9. Continuing Work • Move to recognition of live streaming data • Integrate weather reports and associate a reliability factor with wind direction and speed • Continue fine tuning the system to improve recognition