Random Forest for improved analysis efficiency in passive acoustic monitoring

Abstract Passive acoustic monitoring often leads to large quantities of sound data which are burdensome to process, such that the availability and cost of expert human analysts can be a bottleneck and make ecosystem or landscape-scale projects infeasible. This manuscript presents a method for rapidly analyzing the results of band-limited energy detectors, which are commonly used for the detection of passerine nocturnal flight calls, but which typically are beset by high false positive rates. We first manually classify a subset of the detected events as signals of interest or false detections. From that subset, we build a Random Forest model to eliminate most of the remaining events as false detections without further human inspection. The overall reduction in the labor required to separate signals of interest from false detections can be 80% or more. Additionally, we present an R package, flightcallr, containing functions which can be used to implement this new workflow.

[1]  David W. Armitage,et al.  A comparison of supervised learning techniques in the classification of bat echolocation calls , 2010, Ecol. Informatics.

[2]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[3]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[4]  M. Morrison,et al.  Assessing Impacts of Wind-Energy Development on Nocturnally Active Birds and Bats: A Guidance Document , 2007 .

[5]  John A Hildebrand,et al.  Classification of behavior using vocalizations of Pacific white-sided dolphins (Lagenorhynchus obliquidens). , 2011, The Journal of the Acoustical Society of America.

[6]  Jean,et al.  Comparaison entre des dénombrements auditifs de passereaux la nuit et la réflectivité d ’ un radar de surveillance météorologique Canadien , 2010 .

[7]  Andrew Farnsworth,et al.  FLIGHT CALLS AND THEIR VALUE FOR FUTURE ORNITHOLOGICAL STUDIES AND CONSERVATION RESEARCH , 2005 .

[8]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[9]  Marc O. Lammers,et al.  Comparison of real-time and post-cruise acoustic species identification of dolphin whistles using ROCCA (Real-time Odontocete Call Classification Algorithm) , 2011 .

[10]  Thierry Aubin,et al.  SEEWAVE, A FREE MODULAR TOOL FOR SOUND ANALYSIS AND SYNTHESIS , 2008 .

[11]  Chin-Chuan Han,et al.  Automatic Classification of Bird Species From Their Sounds Using Two-Dimensional Cepstral Coefficients , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  A. Farnsworth,et al.  A comparison of nocturnal call counts of migrating birds and reflectivity measurements on Doppler radar , 2004 .

[14]  David Yarowsky,et al.  Unsupervised Word Sense Disambiguation Rivaling Supervised Methods , 1995, ACL.