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
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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