| Resumen |
In nature many animals use sound communication to exchange information. For instance, in the aquatic environment, marine mammals, including whales, depend on sound for both social interactions and to locate prey. For example, the use of passive acoustics to detect and classify species in-situ provides a means of identifying a species in their habitat, reveals their behavior as well as the population density. Automatic classification of marine mammal sounds is perhaps the most challenging task in the field of animal bioacoustics due to the unknown statistical signal properties, as well as the use of different recording systems and low signal to noise ratio (SNR) conditions, among others. Such discrepancies often lead to sub-optimal system performance.
This work evaluates different architectures for automatic classification of eleven marine mammal species found in the Gulf of Mexico, which is home to a high diversity of organisms. The model proposed herein could be useful to monitor, reduce, and avoid some human activities which occur in areas inhabited by protected species. |
