PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Anders Meng, Peter Ahrendt and Jan Larsen
In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 19-23 March 2005, Philadelphia, USA.


Many different short-time features, using time windows in the size of 10-30 ms, have been proposed for music segmentation, retrieval and genre classification. However, often the available time frame of the music to make the actual decision or comparison (the decision time horizon) is in the range of seconds instead of milliseconds. The problem of making new features on the larger time scale from the short-time features (feature integration) has only received little attention. This paper investigates different methods for feature integration and late information fusion for music genre classification. A new feature integration technique, the AR model, is proposed and seemingly outperforms the commonly used mean-variance features.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:277
Deposited By:Anders Meng
Deposited On:23 November 2004