On enabling techniques for personal audio content management
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Audio query by example using similarity measures between probability density functions of features
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on scalable audio-content analysis
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Calculating the similarity estimates between the query sam- ple and the database samples becomes an exhaustive task with large, usually continuously updated multimedia databases. In this paper, a fast and low complexity transformation from the original feature space into k-dimensional vector space and clustering are proposed to alleviate the problem. First k key- samples are chosen randomly from the database. These sam- ples and a distance function specify the transformation from the series of feature vectors into k-dimensional vector space where database (re)clustering can be done fast with plural- ity of traditional clustering technique whenever required. In the experiments, similarity between the samples was calcu- lated by using the Euclidean distance between their associated feature vector probability density functions. The k-means al- gorithm was used to cluster the transformed samples in the vector space. The experiments show that considerable time and computational savings are achieved while there is only a marginal drop in performance.