1. Statistical automatic identification of microchiroptera from echolocation calls Lessons learned from human automatic speech recognition
Mark D. Skowronski and John G. Harris
Computational Neuro-Engineering Lab
Electrical and Computer Engineering
University of Florida
Gainesville, FL, USA
November 19, 2004

2. Overview Motivations for bat acoustic research
Review bat call classification methods
Contrast with 1970s human ASR
Experiments
Conclusions

3. Bat research motivations
Bats are among:
the most diverse,
the most endangered,
and the least studied mammals.
Close relationship with insects
agricultural impact
disease vectors
Acoustical research non-invasive, significant domain (echolocation)
Simplified biological acoustic communication system (compared to human speech)

4. Echolocation calls Features (holistic) Frequency extrema Duration
Shape
# harmonics
Call interval
Mexican free-tailed calls, concatenated

5. Current classification methods
Expert spectrogram readers
Manual or automatic feature extraction
Comparison with exemplar spectrograms
Automatic classification
Decision trees
Discriminant function analysis
Parallels the knowledge-based approach to human ASR from the 1970s (acoustic phonetics, expert systems, cognitive approach).

6. Acoustic phonetics Bottom up paradigm
DH AH F UH T B AO L G EY EM IH Z OW V ER
Bottom up paradigm
Frames, boundaries, groups, phonemes, words
Manual or automatic feature extraction
Determined by experts to be important for speech
Classification
Decision tree, discriminant functions, neural network, Gaussian mixture model, Viterbi path

7. Acoustic phonetics limitations
Variability of conversational speech
Complex rules, difficult to implement
Feature estimates brittle
Variable noise robustness
Hard decisions, errors accumulate
Shifted to information theoretic (machine learning) paradigm of human ASR, better able to account for variability of speech, noise.

8. Information theoretic ASR
Data-driven models from computer science
Non-parametric: dynamic time warp (DTW)
Parametric: hidden Markov model (HMM)
Frame-based
Expert information in feature extraction
Models account for feature, temporal variability

9. Data collection UF Bat House, home to 60,000 bats Continuous recording
Mexican free-tailed bat (vast majority)
Evening bat
Southeastern myotis
Continuous recording
90 minutes around sunset
~20,000 calls
Equipment:
B&K mic (4939), 100 kHz
B&K preamp (2670)
Custom amp/AA filter
NI 6036E 200kS/s A/D card
Laptop, Matlab

10. Experiment design Hand labels 436 calls (2% of data)
Four classes, a priori: 34, 40, 20, 6%
All experiments on hand-labeled data only
No hand-labeled calls excluded from experiments

11. Experiments Baseline DTW and HMM Features Classifier HMM Zero crossing
MUSIC super resolution frequency estimator
Classifier
Discriminant function analysis, quadratic boundaries
DTW and HMM
Frequency (MUSIC), log energy, first derivatives (HMM only)
HMM
5 states/model
4 Gaussian mixtures/state
diagonal covariances

12. Results Baseline, zero crossing Baseline, MUSIC DTW, MUSIC HMM, MUSIC
Leave one out: 72.5% correct
Repeated trials: 72.5 ± 4% (mean ± std)
Baseline, MUSIC
Leave one out: 79.1%
Repeated trials: 77.5 ± 4%
DTW, MUSIC
Leave one out: %
Repeated trials: 74.1 ± 4%
HMM, MUSIC
Test on train: %

13. Confusion matrices Baseline, zero crossing Baseline, MUSIC DTW, MUSIC1 2 3 4
107 38
72.3% 21
134 16
76.6% 29 57
64.8% 18
72.0%
72.5% 1 2 3 4
110 36
74.3% 12
149
85.1% 18 66
75.0% 20
80.0%
79.1%
DTW, MUSIC
HMM, MUSIC 1 2 3 4
115 29
77.7% 32
131 11
74.9% 5 20 63
71.6% 16
64.0%
74.5% 1 2 3 4
118 25 5
79.7% 10
154 6
88.0% 12 75
85.2%
100%
85.3%

14. Conclusions Human ASR algorithms applicable to bat echolocation calls
Experiments
Weakness: accuracy of class labels
HMM most accurate, undertrained
MUSIC frequency estimate robust, slow
Machine learning
DTW: fast training, slow classification
HMM: slow training, fast classification

15. Further information http://www.cnel.ufl.edu/~markskow
DTW reference:
L. Rabiner and B. Juang, Fundamentals of Speech Recognition, Prentice Hall, Englewood Cliffs, NJ, 1993
HMM reference:
L. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” in Readings in Speech Recognition, A. Waibel and K.-F. Lee, Eds., pp. 267–296. Kaufmann, San Mateo, CA, 1990.