Echolocating bats can rapidly modify frequency modulation (FM) curvatures of their calls when facing challenging echolocation tasks. Frequency parameters, such as start/end/peak frequency, have often been extracted from the time-frequency domain to study the call variation. Even though this kind of signal investigation method reveals important findings, these approaches to analyze bat echolocation calls use bulk parameters, which hide subtleties in the call structure that may be important to the bat. In some cases, calls can have the same start and end frequencies but have different FM curvatures, and subsequently may influence the sensory task performance. In the present study, the authors demonstrate an algorithm using a combination of digital filters, power limited time-frequency information, derivative dynamic time warping, and agglomerative hierarchical clustering to extract and categorize the time-frequency components (TFCs) of 21 calls from Brazilian free-tailed bat (Tadarida brasiliensis) to quantitatively compare FM curvatures. The detailed curvature analysis shows an alternative perspective to look into the TFCs and hence serves as the preliminary step to understand the adaptive call design of bats.
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