The Effect of Features on Clustering in Audio Surveillance

Vesa S. The Effect of Features on Clustering in Audio Surveillance. In: AES Conference: 30th International Conference: Intelligent Audio Environments. Audio Engineering Society; 2007. Paper 11. Available from: https://aes.org/publications/elibrary-page/?id=13909

@inproceedings{Vesa2007_13909,
  author = {Vesa, Sampo},
  title = {{The Effect of Features on Clustering in Audio Surveillance}},
  booktitle = {AES Conference: 30th International Conference: Intelligent Audio Environments},
  note = {Paper 11},
  year = {2007},
  month = mar,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=13909}
}

TY  - CPAPER
TI  - The Effect of Features on Clustering in Audio Surveillance
AU  - Vesa, Sampo
T2  - AES Conference: 30th International Conference: Intelligent Audio Environments
M1  - Paper 11
PY  - 2007
DA  - 2007/03/06
UR  - https://aes.org/publications/elibrary-page/?id=13909
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -