科研之友
微信
新浪微博
Facebook
分享链接
摘要
Conflicts between wildlife and agriculture are increasing and may result in severe financial losses. The effectiveness of frightening devices is often highly variable, due to habituation to disruptive or disturbing stimuli. An adaptive frightening device, capable of focusing and altering the disruptive stimuli to specific species could potentially reduce the risk of habituation and thereby provide an effective tool against geese and other conflict species. Automated species detection could form a critical component of an adaptive frightening device. In this article we present a method for detection and recognition of conflict species, including rooks and three species of geese, based on their vocalizations. The detection of conflict species is based on a multiple hypothesis algorithm, where conflict species and background are statistically modeled by Gaussian Mixture Models (GMMs), which have been trained with labeled data. Subsequent individual species recognition is accomplished through maximum likelihood evaluation of GMMs trained on labeled species data. Mel Frequency Cepstral Coefficients (MFCC) are used as features for the both the detection and recognition models. The proposed detection algorithm shows strong performance, with a detection rate of 0.98 +/- 0.01, and the species recognition results in true positive rates from 0.87 to 0.97 amongst the four species evaluated in this article. Accuracy, precision and false alarm rates have been used to evaluate the performance of the proposed species recognition algorithm. Based on high detection rate of conflict species (0.98 +/- 0.01) and low false alarm rates (0.02-0.04), we conclude that it is possible to implement robust species specific detection, based on vocalizations, and as such, it can be used as an integrated part of a wildlife management system.
- 出版日期2014-1
