| Issue |
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|
|
|---|---|---|
| Article Number | 02002 | |
| Number of page(s) | 7 | |
| Section | Session 2: Bioinformatics | |
| DOI | https://doi.org/10.1051/itmconf/20171202002 | |
| Published online | 05 September 2017 | |
A Comparison Study to Identify Birds Species Based on Bird Song Signals
Department of Computer Science, Sam Houston State University, Huntsville, TX 77382, USA
* xxg004@shsu.edu
** liu@shsu.edu
this paper presents a comparison study in automatically identifying bird species based on bird acoustic signals, using audio files from XENO-CANTO online database. The features including Mel-Frequency Cepstral Coefficients (MFCC), geo-related meta-features, and the integration are compared. The learning classifiers Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Ensemble Learning are examined. Our experimental results show that in the comparison study, ensemble learning using discriminant learner with the integration of MFCC features and geo meta-features obtains the best detection performance.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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