Bird Classification using Ensemble Classifiers

This working note summarizes our submission to the LifeCLEF 2014 Bird Task which combines the outputs from a Python and Matlab classification system. The features used for both systems include Mel-Frequency Cepstral Coefficients (MFCC), time-averaged spectrograms and the provided meta-data. The Python subsystem combines a large ensemble of different classifiers with different subsets of the features while the Matlab subsystem is an ensemble of the Random Forest and Linear Discriminant Analysis (LDA) classifiers using local spectral and meta features. By combining this disparate set of features and classifiers, we managed to achieve a Mean Average Precision (MAP) score that is far superior to what is possible with any single classifier.