Integrating heterogeneous reatures for efficient content based music retrieval

Authors:
Jialie Shen;John Shepherd;Anne H.H. Ngu
Affiliations:
University of New South Wales, Sydney, Australia;University of New South Wales, Sydney, Australia;Texas State University, San Marcos, TX
Venue:
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Year:
2004

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Abstract

In this paper, we present a novel feature extraction method facilitating efficient content-based music retrieval and classification, called InMAF. The goal of our approach is to allow straightforward incorporation of multiple musical features, such as timbral texture, pitch and rhythm structure, into a single low dimensional vector that is effective for retrieval and classification. Unlike earlier approaches that used only acoustic properties as the basis for retrieval, our approach can easily incoporate human music perception to improve accuracy of retrieval and classification process. The superiority of our method is demonstrated by comparing it with state-of-the-art approaches in the areas of music classification (using a variety of machine learning algorithms), query effectiveness and robustness against audio distortion.