A summary of the poster presented at the

1. Automatic detection and classification of Microchiropteran echolocation calls: Why the current technology is wrong and what can be done about it A summary of the poster presented at the North American Symposium on Bat Research, Sacramento, CA, Oct. 19-22, 2005 Mark D. Skowronski and John G. Harris Computational Neuro-Engineering Lab Electrical and Computer Engineering University of Florida, Gainesville, FL, USA October 25, 2005

2. Overview • Review of bat call analysis • Machine learning methods • Latest results (JASA manuscript) • Future work • Conclusions

3. Bat call analysis • Most bats constantly emit acoustic chirps during flight for navigation/hunting: echolocation • Echolocation calls provide useful information: • Presence • Family/genus/species • Flight characteristics • Hunting strategies, social interactions • Automated analysis desired: • Volumous data: 8 hrs of recordings, 16 bit, 200 kHz produces 11.5 GBytes data • Repeatable, objective, accurate, robust, fast

4. Conventional automated methods • For detection: • Frame-based, ~1ms duration • Frame energy compared to threshold • No frequency/temporal information • For classification: • Global call features from frames: min/max frequency, duration, frequency at peak energy • Discriminant function analysis on global features • No amplitude information, limited power in DFA model

5. Machine learning methods • For detection: • Frame-based features: log spectral peak, frequency at spectral peak, first- and second-order temporal derivatives, spectral mean subtraction • Gaussian mixture model (GMM) for features of hand-labeled calls AND background noise • Log likelihood difference, between call and background GMMs, compared to threshold • For classification: • Same frame-based features as for detection • GMM or hidden Markov model (HMM) trained from hand-labeled calls, one model for each species • Classifier output: label from model with maximum log likelihood

6. Detection experiment • Database of bat calls • 5 species, 5 recording locations, 3 systems • Pipistrellus bodenheimeri, Dead Sea, Pettersson • Molossus molossus, West Indies, Pettersson • Lasiurus borealis, Ontario, Avisoft • Lasiurus cinereus semotus, Hawaii, Avisoft • Tadarida brasiliensis, Gainesville, Custom • 2941 hand-labeled calls • Detection experiment design • Discrete events: 20-ms bins • Discrete outcomes: Yes or No: does a bin contain any part of a bat call?

7. Detector results

8. Detector examples Each gray column is a hand-labeled call from a pass of 25 calls from L. borealis. The black horizontal lines represent the thresholds for equal sensitivity/specificity.

9. Classification experiment • Database of bat calls, same as for detection experiment • Cross-validation design • 50% test/train sets from hand-labeled calls • 20 trials

10. Classification results Results over 20 trials of 50% random test/train. GMM and HMM results statistically insignificant (t-test, p>0.9).

11. Future work • Repeat with zero-crossing data. • More species, more locations. • Optimize experiment parameters: # Gaussians, # states, frame size/ rate, derivative size, … • Detection/classification of pass of calls. • Microphone array data for direction of arrival. • Speaker identification, habitat variations, regional variations. • Collaborate with more bat researchers to get data.

12. Conclusions • GMM detection error 8x lower than broadband energy detector. • GMM/HMM classification error 28x lower than DFA baseline. • Machine learning methods superior to conventional methods because: • More information used • More powerful models • Cross-discipline work ripe for methods developed for human speech.

13. Further information • http://www.cnel.ufl.edu/~markskow • markskow@cnel.ufl.edu Acknowledgements Bat data kindly provided by: Brock Fenton, U. of Western Ontario, Canada