J. Acoustical Society of America, vol. 117, no. 4, p. 2552, Vancouver, CA, Apr. 2005
Automatic detection of microchiroptera echolocation calls from field recordings
using machine learning algorithms
M. D. Skowronski and J. G. Harris
The authors have recently presented experimental results of applying machine learning
algorithms, used extensively in human automatic speech recognition research (ASR), to
automatic species identification of echolocating bats [Skowronski and Harris, JASA 116 (5),
2639 (2004)]. The results of those experiments demonstrated that frame-based classification,
preferred in ASR, out-performs holistic classification typically employed in automatic
echolocating species identification. The authors have extended the paradigm of machine
learning algorithms to the related problem of bat call detection. A robust automatic bat call
detection algorithm, to replace hand labeling, is required for two reasons: 1) for real-time species
identification in the field, and 2) because hand labeling is subjective, tedious, slow, and errorprone. The current experiments compare various frame-based features (log energy, pitch
estimates, pitch slopes) with several models of detection (matched filters, Gaussian mixtures,
decision trees). Detector sensitivity and specificity are quantified for comparison using handlabeled calls, with considerations of classification requirements for detected calls. That is, a
detector is not penalized for including a short segment of background signal before and after a
hand-labeled call. The results demonstrate the superior performance of the frame-based features
and machine learning detection algorithms compared to conventional features and detection
algorithms.