Detection of frequency-modulated calls using a chirp model

Justin Matthews


Many cetacean vocalisations are tonal and most are frequency-modulated. The detection algorithm presented here breaks the frequency contour into a sequence of elements. Each element is sufficiently short that a linear approximation to the frequency contour can be made. In this way the problem is simplified from that of detection of an unknown signal, to the detection of a known signal (a linear chirp) with unknown parameters. The method of estimation is based on maximum likelihood, and the start frequency, chirp rate and amplitude of each element are estimated. Further analysis is then carried out on groups of concatenated chirps (i.e. calls) to classify them. Results are given on performance for the supplied test recording and for synthetic signals in white noise. The pros of the algorithm are: good detection performance, at least in white noise; high resolution; ease of interpretation; flexibility; data compression. The cons are: computational cost; deterioration, of performance in non-white noise or with amplitude-modulated signals. Further development is needed to reduce errors with overlapping tonal or non-tonal signals. The algorithm is currently being applied to the problem of detecting right whale vocalisations and distinguishing them from those of humpback whales.


Acoustic noise; Algorithms; Approximation theory; Costs; Data compression; Frequency modulation; Linear equations; Mathematical models; Parameter estimation; Polynomials; Problem solving; Signal detection; Cetacean volcalizations; Chirp Models; Computational costs; Frequency-Modulated Calls

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