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
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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:
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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:
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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
Feature extraction examples
Feature extraction examples
Six features: Power, Frequency, P, F P, F
Detection example
Experiment results
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
• http://www.cnel.ufl.edu/~markskow
• markskow@cnel.ufl.edu
Acknowledgements
Bat data kindly provided by:
Brock Fenton, U. of Western Ontario, Canada