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A method for detecting whistles, moans, and other frequency contour sounds
David K Mellinger1, Stephen W Martin, Ronald P Morrissey
1Cooperative Institute for Marine Resources Studies, Oregon State University and Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, 2030 Southeast Marine Science Dr., Newport, Oregon 97365, USA. David.Mellinger@oregonstate.edu
The Journal of the Acoustical Society of America
|June 21, 2011
Summary
A new algorithm detects animal sounds by tracking spectral peaks. This method accurately identifies minke whale "boing" sounds, demonstrating its potential for bioacoustics research.
Area of Science:
- Bioacoustics
- Animal Communication
- Signal Processing
Background:
- Automated detection of animal vocalizations is crucial for ecological monitoring and behavioral studies.
- Existing methods often struggle with complex sounds or require extensive manual tuning.
- Frequency contour sounds, like whistles and moans, are common across many species.
Purpose of the Study:
- To develop and validate an algorithm for detecting frequency contour sounds in acoustic data.
- To optimize algorithm parameters for robust sound detection.
- To assess the algorithm's performance in identifying minke whale 'boing' sounds.
Main Methods:
- An algorithm tracks spectral peaks over time, grouping them into smooth frequency contours.
- Spectrogram analysis and normalization are key components of the detection process.
- A grid search technique is employed to find optimal values for the algorithm's nine parameters.
Main Results:
- The algorithm successfully detected minke whale 'boing' sounds with a 3% false-detection rate at a 25% missed-call rate.
- The method demonstrated effectiveness even in datasets containing interfering sounds, such as humpback whale vocalizations.
- Anecdotal evidence suggests applicability to various marine and terrestrial species, as well as non-animal sounds.
Conclusions:
- The developed frequency contour detection algorithm offers a robust and efficient method for identifying specific animal vocalizations.
- Parameter optimization is essential for maximizing detection performance.
- This algorithm has broad potential applications in bioacoustics and sound analysis across diverse taxa and sound types.
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