AES E-Library

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.

Author: Vesa, Sampo
Affiliation: Helsinki University of Technology (TKK)
AES Conference:30th International Conference: Intelligent Audio Environments (March 2007)
Paper Number:11 Import into BibTeX
Subject:Intelligent Audio Environments

Click to purchase paper or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $20 for non-members, $5 for AES members and is free for E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD 30thPapers)   /30/aes30-000043.pdf

Start a discussion about this paper!