| Paper: | ITT-P1.1 | ||
| Session: | Speech and Language Applications | ||
| Time: | Thursday, May 20, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Industry Technology Track: Speaker Recognition | ||
| Title: | BIRD CLASSIFICATION ALGORITHMS: THEORY AND EXPERIMENTAL RESULTS | ||
| Authors: | Chiman Kwan; Intelligent Automation, Inc. | ||
| Gang Mei; Intelligent Automation, Inc. | |||
| George Zhao; Intelligent Automation, Inc. | |||
| Zhubing Ren; Intelligent Automation, Inc. | |||
| Roger Xu; Intelligent Automation, Inc. | |||
| Vincent Stanford; National Institute of Standards and Technology | |||
| Cedric Rochet; National Institute of Standards and Technology | |||
| Julian Aube; National Institute of Standards and Technology | |||
| K. C. Ho; University of Missouri-Columbia | |||
| Abstract: | To minimize the number of birdstrikes, a common method is to use microphone arrays to monitor and identify dangerous birds near the airport or some critical locations in the airspace. However, it was recognized that the range of existing ground-based acoustic monitoring devices is only limited to a few hundred meters. Moreover, the bird classification performance in low signal-to-noise environments such as airports is not very satisfactory.This paper summarizes the development of a high performance bird classification system using Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM). Experimental results verified the classification performance. | ||
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