Integrating Heterogeneous Features for Efficient Content-based Music Retrieval
Conference Proceeding Article
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.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
CIKM '04 Proceedings of the ACM 13th Conference on Information and Knowledge Management
SHEN, Jialie; John, Shepherd; and Ahh, Ngu.
Integrating Heterogeneous Features for Efficient Content-based Music Retrieval. (2004). CIKM '04 Proceedings of the ACM 13th Conference on Information and Knowledge Management. 154-155. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1237
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