InMAF: Indexing Music Databases via Multiple Acoustic Features
Music information processing has become very important due to the ever-growing amount of music data from emerging applications. In this demonstration, we present a novel approach for generating small but comprehensive music descriptors to facilitate efficient content music management (accessing and...
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sg-smu-ink.sis_research-22312010-12-22T08:24:06Z InMAF: Indexing Music Databases via Multiple Acoustic Features SHEN, Jialie Shepherd, John Ngu, AHH Music information processing has become very important due to the ever-growing amount of music data from emerging applications. In this demonstration, we present a novel approach for generating small but comprehensive music descriptors to facilitate efficient content music management (accessing and retrieval, in particular). Unlike previous approaches that rely on low-level spectral features adapted from speech analysis technology, our approach integrates human music perception to enhance the accuracy of the retrieval and classification process via PCA and neural networks. The superiority of our method is demonstrated by comparing it with state-of-the-art approaches in the areas of music classification query effectiveness, and robustness against various audio distortion/alternatives. 2006-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/1232 info:doi/10.1145/1142473.1142587 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing |
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Databases and Information Systems Numerical Analysis and Scientific Computing SHEN, Jialie Shepherd, John Ngu, AHH InMAF: Indexing Music Databases via Multiple Acoustic Features |
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Music information processing has become very important due to the ever-growing amount of music data from emerging applications. In this demonstration, we present a novel approach for generating small but comprehensive music descriptors to facilitate efficient content music management (accessing and retrieval, in particular). Unlike previous approaches that rely on low-level spectral features adapted from speech analysis technology, our approach integrates human music perception to enhance the accuracy of the retrieval and classification process via PCA and neural networks. The superiority of our method is demonstrated by comparing it with state-of-the-art approaches in the areas of music classification query effectiveness, and robustness against various audio distortion/alternatives. |
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text |
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SHEN, Jialie Shepherd, John Ngu, AHH |
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SHEN, Jialie Shepherd, John Ngu, AHH |
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SHEN, Jialie |
title |
InMAF: Indexing Music Databases via Multiple Acoustic Features |
title_short |
InMAF: Indexing Music Databases via Multiple Acoustic Features |
title_full |
InMAF: Indexing Music Databases via Multiple Acoustic Features |
title_fullStr |
InMAF: Indexing Music Databases via Multiple Acoustic Features |
title_full_unstemmed |
InMAF: Indexing Music Databases via Multiple Acoustic Features |
title_sort |
inmaf: indexing music databases via multiple acoustic features |
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Institutional Knowledge at Singapore Management University |
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2006 |
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https://ink.library.smu.edu.sg/sis_research/1232 |
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1770570905725960192 |