Automatic detection of microchiroptera echolocation calls from field recordings using machine learning algorithms

Abstract

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, J. Acoust. Soc. Am. 116, 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 error-prone. 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 hand-labeled 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.