@article {57648, title = {Using acoustic metrics to characterize underwater acoustic biodiversity in the Southern Ocean}, year = {2019}, abstract = {

Acoustic metrics (AM) assist our interpretation of acoustic environments by aggregating a complex signal into a unique number. Numerous AM have been developed for terrestrial ecosystems, with applications ranging from rapid biodiversity assessments to characterizing habitat quality. However, there has been comparatively little research aimed at understanding how these metrics perform to characterize the acoustic features of marine habitats and their relation with ecosystem biodiversity. Our objectives were to 1) assess whether AM are able to capture the spectral and temporal differences between two distinct Antarctic marine acoustic environment types (i.e., pelagic vs. on-shelf), 2) evaluate the performance of a combination of AM compared to the signal full frequency spectrum to characterize marine mammals acoustic assemblages (i.e., species richness\–SR\–and species identity) and 3) estimate the contribution of SR to the local marine acoustic heterogeneity measured by single AM. We used 23 different AM to develop a supervised machine learning approach to discriminate between acoustic environments. AM performance was similar to the full spectrum, achieving correct classifications for SR levels of 58\% and 92\% for pelagic and on-shelf sites respectively and \> 88\% for species identities. Our analyses show that a combination of AM is a promising approach to characterize marine acoustic communities. It allows an intuitive ecological interpretation of passive acoustic data, which in the light of ongoing environmental changes, supports the holistic approach needed to detect and understand trends in species diversity, acoustic communities and underwater habitat quality.

}, keywords = {Acoustic metrics, Antarctic, community composition, marine acoustic environments, passive acoustic monitoring, species diversity}, doi = {10.1002/rse2.129}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.129}, author = {Roca, Irene T. and Van Opzeeland, Ilse}, editor = {Pettorelli, Nathalie and Quick, Nicola} } @article {57604, title = {Using acoustic metrics to characterize underwater acoustic biodiversity in the Southern Ocean}, year = {2019}, abstract = {

Acoustic metrics (AM) assist our interpretation of acoustic environments by aggregating a complex signal into a unique number. Numerous AM have been developed for terrestrial ecosystems, with applications ranging from rapid bio- diversity assessments to characterizing habitat quality. However, there has been comparatively little research aimed at understanding how these metrics perform to characterize the acoustic features of marine habitats and their relation with ecosystem biodiversity. Our objectives were to 1) assess whether AM are able to capture the spectral and temporal differences between two distinct Antarctic marine acoustic environment types (i.e., pelagic vs. on-shelf), 2) evaluate the performance of a combination of AM compared to the signal full frequency spectrum to characterize marine mammals acoustic assemblages (i.e., species richness\–SR\–and species identity) and 3) estimate the contribution of SR to the local marine acoustic heterogeneity measured by single AM. We used 23 differ- ent AM to develop a supervised machine learning approach to discriminate between acoustic environments. AM performance was similar to the full spec- trum, achieving correct classifications for SR levels of 58\% and 92\% for pelagic and on-shelf sites respectively and \> 88\% for species identities. Our analyses show that a combination of AM is a promising approach to characterize marine acoustic communities. It allows an intuitive ecological interpretation of passive acoustic data, which in the light of ongoing environmental changes, supports the holistic approach needed to detect and understand trends in species diver- sity, acoustic communities and underwater habitat quality.

}, keywords = {Acoustic metrics, Antarctic, community composition, marine acoustic environments, passive acoustic monitoring, species diversity}, doi = {10.1002/rse2.129}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.129}, author = {Roca, Irene T. and Van Opzeeland, Ilse}, editor = {Pettorelli, Nathalie and Quick, Nicola} } @article {53103, title = {On the reliability of acoustic annotations and automatic detections of Antarctic blue whale calls under different acoustic conditions}, journal = {The Journal of the Acoustical Society of America}, volume = {144}, year = {2018}, month = {Jan-08-2018}, pages = {740 - 754}, abstract = {

Evaluation of the performance of computer-based algorithms to automatically detect mammalian vocalizations often relies on comparisons between detector outputs and a reference data set, generally obtained by manual annotation of acoustic recordings. To explore the reproducibility of these annotations, inter- and intra-analyst variability in manually annotated Antarctic blue whale (ABW) Z-calls are investigated by two analysts in acoustic data from two ocean basins representing different scenarios in terms of call abundance and background noise. Manual annotations exhibit strong inter- and intra-analyst variability, with less than 50\% agreement between analysts. This variability is mainly caused by the difficulty of reliably and reproducibly distinguishing single calls in an ABW chorus made of overlaying distant calls. Furthermore, the performance of two automated detectors, based on spectrogram correlation or subspace-detection strategy, is evaluated by comparing detector output to a \“conservative\” manually annotated reference data set, which comprises only analysts\&$\#$39; matching events. This study highlights the need for a standardized approach for human annotations and automatic detections, including a quantitative description of their performance, to improve the comparability of acoustic data, which is particularly relevant in the context of collaborative approaches in collecting and analyzing large passive acoustic data sets.

}, issn = {0001-4966}, doi = {10.1121/1.5049803}, url = {http://asa.scitation.org/doi/10.1121/1.5049803}, author = {Leroy, Emmanuelle C. and Thomisch, Karolin and Royer, Jean-Yves and Boebel, Olaf and Van Opzeeland, Ilse} }