@article {ferguson_acoustic_2023,
	title = {Acoustic indices respond to specific marine mammal vocalizations and sources of anthropogenic noise},
	journal = {Frontiers in Marine Science},
	volume = {10},
	year = {2023},
	abstract = {Using passive acoustic methods for biodiversity conservation and effective ecosystem monitoring is hindered by laborious, human-mediated processes of accurately identifying biologic and anthropogenic sounds within large datasets. Soundscape ecology provides a potential means of addressing this need through the use of automated acoustic-based biodiversity indices, which show promise in representing biodiversity in terrestrial environments. However, the direct relationship between specific underwater sounds and acoustic index measurements are largely unexplored. Using passive acoustic data collected from three broadband hydrophones within the Ocean Observatories Initiative{\textquoteright}s cabled arrays in the Pacific northwest, we identified periods of vocalizing marine mammals and sources of anthropogenic noise. Automated calculations of seven acoustic indices were compared across biologic and anthropogenic sound type and call parameters. Although several index measurements did not vary significantly, the Acoustic Complexity Index (ACI) measurements increased in response to echolocation clicks from sperm whales (Physeter macrocephalus) and burst pulses originating from unidentified delphinid species. Measurements of the Bioacoustic Index (BI) decreased dramatically in response to sperm whale echolocation clicks, a more obvious trend when loud clicks were parsed from moderate and quiet clicks. Correlations coefficient and confidence interval values between ACI and BI measurements and call characteristics from sperm whales indicate a moderate to strong relationship, which was not found in correlations with delphinid calls. A generalized linear mixed-effect model indicated multiple species and sound types contribute significantly to the variation of several index measurements. Noise generated by passing ships consistently resulted in decreased values for the Normalized Difference Soundscape Index (NDSI) and Total Entropy (H) as compared to quiet periods and periods with vocalizing marine mammals. These findings provide information on the relationship between several acoustic indices and specific underwater sounds produced by marine mammals and anthropogenic sources. This ground-truthing endeavor expands the understanding of acoustic indices and their potential use as a tool for conservation and ecosystem health management purposes.},
	issn = {2296-7745},
	doi = {10.3389/fmars.2023.1025464},
	url = {https://www.frontiersin.org/articles/10.3389/fmars.2023.1025464},
	author = {Ferguson, Elizabeth L. and Clayton, Hannah M. and Sakai, Taiki}
}
@article {277,
	title = {Development of deep neural networks for marine mammal call detection using an open-source, user friendly tool},
	journal = {The Journal of the Acoustical Society of America},
	volume = {151},
	year = {2022},
	month = {2022/04//},
	pages = {A28},
	abstract = {As the collection of large acoustic datasets used to monitor marine mammals increases, so too does the need for expedited and reliable detection of accurately classified bioacoustic signals. Deep learning methods of detection and classification are increasingly proposed as a means of addressing this processing need. These image recognition and classification methods include the use of a neural networks that independently determine important features of bioacoustic signals from spectrograms. Recent marine mammal call detection studies report consistent performance even when used with datasets that were not included in the network training. We present here the use of DeepSqueak, a novel open-source tool originally developed to detect and classify ultrasonic vocalizations from rodents in a low-noise, laboratory setting. We have trained networks in DeepSqueak to detect marine mammal vocalizations in comparatively noisy, natural acoustic environments. DeepSqueak utilizes a regional convolutional neural network architecture within an intuitive graphical user interface that provides automated detection results independent of acoustician expertise. Using passive acoustic data from two hydrophones on the Ocean Observatories Initiative{\textquoteright}s Coastal Endurance Array, we developed networks for humpback whales, delphinids, and fin whales. We report performance and limitations for use of this detection method for each species.},
	isbn = {0001-4966},
	doi = {10.1121/10.0010547},
	url = {https://asa.scitation.org/doi/abs/10.1121/10.0010547},
	author = {Ferguson, Elizabeth L. and Sugarman, Peter and Coffey, Kevin R. and Pettis Schallert, Jennifer and Alongi, Gabriela C.}
}
