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 dictionarybased 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 15000 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.

This paper investigates the evolution of spectral properties observed in Cuvier's beaked whale (Ziphius cavirostris) click trains recorded by fixed hydrophones in the Gulf of Mexico. In the context of deep water and high-frequency sounds and observed inter-click intervals, the authors assumed that the main effect responsible for the modification of the spectral content between adjacent clicks in the same click train is the source beam pattern. The spectral structure is studied by using the Wigner-Ville time-frequency distribution and is compared with the conventional Fourier spectrogram. The results show that the observed Cuvier's beaked whale clicks are a superposition of upsweep and downsweep chirps, unlike the currently accepted upsweep only structure of beaked whale clicks in bioacoustics literature. The spectral structure variations simulated by using a flat circular piston model as a beam pattern transmission model are consistent with the evolution of spectral click properties observed in experimental data. A better understanding of the properties of observed echolocation clicks of Cuvier's beaked whales will provide useful information for click annotations and, therefore, will contribute to improving accuracy of detecting, classifying, tracking, and estimating the density of Cuvier's beaked whales.

Passive acoustic monitoring has been successfully used to study deep-diving marine mammal populations. To assess regional population trends of sperm whales in the northern Gulf of Mexico (GoM), including impacts of the Deepwater Horizon platform oil spill in 2010, the Littoral Acoustic Demonstration Center-Gulf Ecological Monitoring and Modeling (LADC-GEMM) consortium collected broadband acoustic data in the Mississippi Valley/Canyon area between 2007 and 2017 using bottom-anchored moorings. These data allow the inference of short-term and long-term variations in site-specific abundances of sperm whales derived from their acoustic activity. A comparison is made between the abundances of sperm whales at specific sites in different years before and after the oil spill by estimating the regional abundance density. The results show that sperm whales were present in the region throughout the entire monitoring period. A habitat preference shift was observed for sperm whales after the 2010 oil spill with higher activities at sites farther away from the spill site. A comparison of the 2007 and 2015 results shows that the overall regional abundance of sperm whales did not recover to pre-spill levels. The results indicate that long-term spatially distributed acoustic monitoring is critical in characterizing sperm whale population changes and in understanding how environmental stressors impact regional abundances and the habitat use of sperm whales.

Pre-spill and post-spill passive acoustic data collected by multiple fixed acoustic sensors monitoring about 2400 km2 area to the west of the Deepwater Horizon oil spill in the northern Gulf of Mexico (GoM) were analyzed to understand long term local density trends and habitat use by different species of beaked whales. The data were collected in the Mississippi Valley/Canyon area between 2007 and 2017. A multistage algorithm based on unsupervised machine learning was developed to detect and classify different species of beaked whales and to derive species- and site-specific densities in different years before and after the oil spill. The results suggest that beaked whales continued to occupy and feed in these areas following the Deepwater Horizon oil spill thus raising concerns about (1) potential long-term effects of the spill on these species and (2) the habitat conditions after the spill. The average estimated local density of Cuvier’s beaked whales at the closest site, about 16 km away from the spill location showed statistically significant increase from July 2007 to September 2010, and then from September 2010 to 2015. This is the first acoustic study showing that Gervais’ beaked whales are predominantly present at the shallow site and that Cuvier’s species dominate at two deeper sites, supporting the habitat division (ecological niche) hypothesis. The findings call for continuing high-spatial-resolution long-term observations to fully characterize baseline beaked whale population and habitat use, to understand the causes of regional migrations, and to monitor the long-term impact of the spill.