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Enhancing Bat Echolocation Call Detection & Classification

Summary poster from North American Symposium on Bat Research discussing problems with current technology, machine learning methods, experiments, results, and future work in improving automated analysis of Microchiropteran echolocation calls. Collaboration opportunities also highlighted.

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Enhancing Bat Echolocation Call Detection & Classification

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  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

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