No abstract

Bearded seal (Erignathus barbatus) calls were recorded using autonomous passive acoustic recorders deployed in the northeastern Chukchi Sea between October 2007 and October 2010. Continuous acoustic data were acquired during summer (August to mid-October), and overwinter data (mid-October through July) were acquired on a duty cycle of 40/48 min every 4 h. We investigated the spatio-temporal distribution and acoustic behavior of vocalizing bearded seals in this multiyear data set. Peaks in calling occurred in spring, coinciding with the mating period, and calls stopped abruptly in late June/early July. Fewer calls were detected in summer, and the vocal presence of seals increased with the formation of pack ice in winter. Vocal activity was higher at night than during the day, with a peak around 0400 (AKST). Monthly patterns in proportional use of each call type and call duration were examined for the first time. The proportion and duration of AL1(T) and AL2(T) call types increased during the mating period, suggesting that males advertise their breeding condition by producing those specific longer trills. The observed seasonal and diel trends were consistent between years. These results improve our understanding of occurrence and acoustic behavior of bearded seals across the northeastern Chukchi Sea.

Although many fish are soniferous, few of their sounds have been identified, making passive acoustic monitoring (PAM) ineffective. To start addressing this issue, a portable 6-hydrophone array combined with a video camera was assembled to catalog fish sounds in the wild. Sounds are detected automatically in the acoustic recordings and localized in three dimensions using time-difference of arrivals and linearized inversion. Localizations are then combined with the video to identify the species producing the sounds. Uncertainty analyses show that fish are localized near the array with uncertainties < 50 cm. The proposed system was deployed off Cape Cod, MA and used to identify sounds produced by tautog (Tautoga onitis), demonstrating that the methodology can be used to build up a catalog of fish sounds that could be used for PAM and fisheries management.

No abstract

Monitoring blue and fin whales summering in the St. Lawrence Estuary with passive acoustics requires call recognition algorithms that can cope with the heavy shipping noise of the St. Lawrence Seaway and with multipath propagation characteristics that generate overlapping copies of the calls. In this paper, the performance of three time-frequency methods aiming at such automatic detection and classification is tested on more than 2000 calls and compared at several levels of signal-to-noise ratio using typical recordings collected in this area. For all methods, image processing techniques are used to reduce the noise in the spectrogram. The first approach consists in matching the spectrogram with binary time-frequency templates of the calls (coincidence of spectrograms). The second approach is based on the extraction of the frequency contours of the calls and their classification using dynamic time warping (DTW) and the vector quantization (VQ) algorithms. The coincidence of spectrograms was the fastest method and performed better for blue whale A and B calls. VQ detected more 20 Hz fin whale calls but with a higher false alarm rate. DTW and VQ outperformed for the more variable blue whale D calls.