The Effect of Features on Clustering in Audio Surveillance
The effect of the choice of features on unsupervised clustering in audio surveillance is investigated. The importance of individual features in a larger feature set is first analyzed by examining the component loadings in principal component analysis (PCA). The individual sound events are then assigned into clusters using the self-tuning spectral clustering and the classical K-means algorithms. A weighted version of the original set is used, where the weights have been optimized by a genetic algorithm (GA) for maximally error-free clustering. The weighted feature set expectedly outperforms the original feature set and its PCA-reduced version. Insight into the importance of individual features is also gained.
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