Automatic detection of microchiroptera echolocation calls from field recordings using machine learning algorithms Mark D. Skowronski and John G. Harris Computational Neuro-Engineering Lab Electrical and Computer Engineering University of Florida, Gainesville, FL, USA May 19, 2005

Overview Motivations for acoustic bat detection Machine learning paradigm Detection experiments Conclusions

Bat detection motivations Bats are among the most diverse yet least studied mammals (~25% of all mammal species are bats). Bats affect agriculture and carry diseases (directly or through parasites). Acoustical domain is significant for echolocating bats and is non-invasive. Recorded data can be volumous  automated algorithms for objective and repeatable detection & classification desired.

Conventional methods Conventional bat detection/classification parallels acoustic-phonetic paradigm of automatic speech recognition from 1970s. Characteristics of acoustic phonetics: –Originally mimicked human expert methods –First, boundaries between regions determined –Second, features for each region were extracted –Third, features compared with decision trees, DFA Limitations: –Boundaries ill-defined, sensitive to noise –Many feature extraction algorithms with varying degrees of noise robustness

Machine learning Acoustic phonetics gave way to machine learning for ASR in 1980s: Advantages: –Decisions based on more information –Mature statistical foundation for algorithms –Frame-based features, from expert knowledge –Improved noise robustness For bats: increased detection range

Detection experiments Database of bat calls –7 different recording sites, 8 species –1265 hand-labeled calls (from spectrogram readings) Detection experiment design –Discrete events: 20-ms bins –Discrete outcomes: Yes or No: does a bin contain any part of a bat call?

Detectors Baseline –Threshold for frame energy Gaussian mixture model (GMM) –Model of probability distribution of call features –Threshold for model output probability Hidden Markov model (HMM) –Similar to GMM, but includes temporal constraints through piecewise-stationary states –Threshold for model output probability along Viterbi path

Feature extraction Baseline –Normalization: session noise floor at 0 dB –Feature: frame power Machine learning –Blackman window, zero-padded FFT –Normalization: log amplitude mean subtraction From ASR: ~cepstral mean subtraction Removes transfer function of recording environment Mean across time for each FFT bin –Features: Maximum FFT amplitude, dB Frequency at maximum amplitude, Hz First and second temporal derivatives (slope, concavity)

Feature extraction examples

Six features: Power, Frequency,  P,  F  P,  F

Detection example

Experiment results

Conclusions Machine learning algorithms improve detection when specificity is high (>.6). HMM slightly superior to GMM, uses more temporal information, but slower to train/test. Hand labels determined using spectrogram, biased towards high-power calls. Machine learning models applicable to other species.

Bioacoustic applications To apply machine learning to other species: –Determine ground truth training data through expert hand labels –Extract relevant frame-based features, considering domain-specific noise sources (echos, propellor noise, other biological sources) –Train models of features from hand-labeled data –Consider training “silence” models for discriminant detection/classification

Further information Acknowledgements Bat data kindly provided by: Brock Fenton, U. of Western Ontario, Canada