RESUMEN
This paper presents an automatic classification method dedicated to mysticete calls. This method relies on sparse representations which assume that mysticete calls lie in a linear subspace described by a dictionary-based representation. The classifier accounts for noise by refusing to assign the observed signal to a given class if it is not included into the linear subspace spanned by the dictionaries of mysticete calls. Rejection of noise is achieved without feature learning. In addition, the proposed method is modular in that, call classes can be appended to or removed from the classifier without requiring retraining. The classifier is easy to design since it relies on a few parameters. Experiments on five types of mysticete calls are presented. It includes Antarctic blue whale Z-calls, two types of "Madagascar" pygmy blue whale calls, fin whale 20 Hz calls and North-Pacific blue whale D-calls. On this dataset, containing 2185 calls and 15 000 noise samples, an average recall of 96.4% is obtained and 93.3% of the noise data (persistent and transient) are correctly rejected by the classifier.
RESUMEN
Matched-field acoustic source localization is a challenging task when environmental properties of the oceanic waveguide are not precisely known. Errors in the assumed environment (mismatch) can cause severe degradations in localization performance. This paper develops a Bayesian approach to improve robustness to environmental mismatch by considering the waveguide Green's function to be an uncertain random vector whose probability density accounts for environmental uncertainty. The posterior probability density is integrated over the Green's function probability density to obtain a joint marginal probability distribution for source range and depth, accounting for environmental uncertainty and quantifying localization uncertainty. Because brute-force integration in high dimensions can be costly, an efficient method is developed in which the multi-dimensional Green's function integration is approximated by one-dimensional integration over a suitably defined correlation measure. An approach to approximate the Green's function covariance matrix, which represents the environmental mismatch, is developed based on modal analysis. Examples are presented to illustrate the method and Monte-Carlo simulations are carried out to evaluate its performance relative to other methods. The proposed method gives efficient, reliable source localization and uncertainties with improved robustness toward environmental mismatch.
RESUMEN
This paper addresses the problem of automated detection of Z-calls emitted by Antarctic blue whales (B. m. intermedia). The proposed solution is based on a subspace detector of sigmoidal-frequency signals with unknown time-varying amplitude. This detection strategy takes into account frequency variations of blue whale calls as well as the presence of other transient sounds that can interfere with Z-calls (such as airguns or other whale calls). The proposed method has been tested on more than 105 h of acoustic data containing about 2200 Z-calls (as found by an experienced human operator). This method is shown to have a correct-detection rate of up to more than 15% better than the extensible bioacoustic tool package, a spectrogram-based correlation detector commonly used to study blue whales. Because the proposed method relies on subspace detection, it does not suffer from some drawbacks of correlation-based detectors. In particular, it does not require the choice of an a priori fixed and subjective template. The analytic expression of the detection performance is also derived, which provides crucial information for higher level analyses such as animal density estimation from acoustic data. Finally, the detection threshold automatically adapts to the soundscape in order not to violate a user-specified false alarm rate.
