InMAF: indexing music databases via multiple acoustic features

ABSTRACT

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.

REFERENCES

• 1: The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces, Proceedings of the 15th International Conference on Data Engineering, p.440, March 23-26, 1999
• 2: Tao Li , Mitsunori Ogihara , Qi Li, A comparative study on content-based music genre classification, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860487]
• 3: Thomas M. Mitchell, Machine Learning, McGraw-Hill Higher Education, 1997
• 4: [4] J. Shen, J. Shepherd, and A. Ngu. Towards effective content based music retrieval with multiple acoustic feature combination. In IEEE Trans. on Multimedia, 2006.
• 5: [5] T. Tolonen and M. Karjalainen. A computationally efficient multipitch analysis model. IEEE Trans. on Speech and Audio Processing, 8(6):708-716, July 2000.
• 6: [6] G. Tzanetakis and P. Cook. Musical genre classification of audio signals. IEEE Trans. on Speech and Audio Processing, 10(5):292-302, July 2002.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

General Terms:
Human Factors, Management, Performance

Keywords:
indexing, music databases, retrieval