@article {53307, title = {The use of context in machine learning for bioacoustics}, journal = {The Journal of the Acoustical Society of America}, volume = {144}, year = {2018}, month = {Jan-09-2018}, pages = {1728 - 1728}, abstract = {

Biological acoustic signals are often produced in context. Contextual information includes things such as timing between calls, conspecific or interspecies cues, and physical environmental cues such as sunrise or sunset. We show how some forms of contextual information can be used to improve the results of detection and classification tasks for biological acoustic signals. We examine how context can be used to improve labeled data, resulting in more-accurate classification results, as well as how learners can exploit context directly. We demonstrate these improvements via two bioacoustic detection/classification tasks. The first algorithm detects odontocete echolocation clicks. We used a decision support system that allowed analysts to label echolocation clicks using between call timing cues as well as other measurements and found that deep learners trained with these high quality data are able to detect clicks in adverse environments. The second algorithm applies contextual information surrounding North Atlantic right whale upcalls to improve precision and recall. [This work was supported by ONR Grant Nos. N00014-17-1-2867 and N00014-15-1-2299.]

}, issn = {0001-4966}, doi = {10.1121/1.5067665}, url = {http://asa.scitation.org/doi/10.1121/1.5067665}, author = {Roch, Marie A. and Baumann-Pickering, Simone and Cholewiak, Danielle and Fleishman, Erica and Frasier, Kaitlin E. and Glotin, Herv{\'e} and Helble, Tyler A. and John A. Hildebrand and Klinck, Holger and Lindeneau, Scott and Liu, Xiaobai and Nosal, Eva-Marie and Palmer, Kaitlin and Shiu, Yu and Singh, Gurisht} } @article {15452, title = {Acoustic monitoring for conservation in tropical forests: examples from forest elephants}, journal = {Methods in Ecology and Evolution}, year = {2017}, month = {Jan-01-2017}, abstract = {

1. The accelerating loss of biodiversity worldwide demands effective tools for monitoring animal populations and informing conservation action. In habitats where direct observation is difficult (rain forests, oceans), or for cryptic species (shy, nocturnal), passive acoustic monitoring (PAM) provides cost-effective, unbiased data collec- tion. PAM has broad applicability in terrestrial environments, particularly tropical rain forests.


2. Using examples from studies of forest elephants in Central African rainforest,we show how PAM can be used to investigate cryptic behaviour, mechanisms of communication, estimate population size, quantify threats, and assess the efficacy of conservation strategies. We discuss the methodologies, requirements, and challenges of obtaining these data using acoustics. Where applicable, we compare these methods to more traditional approaches.


3. While PAM methods and associated analysis are maturing rapidly, mechanisms are needed for processing the dense raw data efficiently with standard computer hardware, speeding development of detection algorithms, and harnessing communication networks to move data from the field to research facilities.


4. Passive acoustic monitoring is a viable and cost-effective tool for conservation and should be incorporated in monitoring schemes much more broadly. The capability to quickly assess changes in behaviour, population size, and landscape use, simultaneously over large geographical areas, makes this approach attractive for detecting human-induced impacts and for assessing the success of conservation strategies.

}, keywords = {anthropogenic impacts, anti-poaching, bioacoustics, cryptic species, density estimate, law enforcement monitoring, Loxodonta cyclotis, passive acoustic monitoring, signal detection, SMART}, doi = {10.1111/2041-210X.12730}, url = {http://doi.wiley.com/10.1111/2041-210X.12730}, author = {Wrege, Peter H. and Rowland, Elizabeth D. and Keen, Sara and Shiu, Yu} }