K-BOX: a query-by-singing based music retrieval system

ABSTRACT

In this paper, we present an efficient query-by-singing based musical retrieval system. We first combine multiple Support Vector Machines by classifier committee learning to segment the sentences from a song automatically. Many new methods in manipulating Mel-Frequency Cepstral Coefficient (MFCC) matrix are studied and compared for optimal feature selection. Experiments show that the 3rd coefficient is the most relevant to music comparison out of 13 coefficients and the proposed simplified MFCC feature is able to achieve a reasonable trade-off between accuracy and efficiency. To improve system efficiency, we re-organize the database by a new two-stage clustering scheme in both time space and feature space. We combine K-means algorithm and dynamic time wrapping similarity measurement for feature space clustering. We also propose a new method for model-selection of K-means algorithm. Experiments show that the proposed approach can achieve more than 30 percent increase in accuracy while speed up more than 16 times in average query time.

References

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Index Terms

• Hardware

  • Communication hardware, interfaces and storage

    • Signal processing systems

  • Robustness

    • Hardware reliability

      • Signal integrity and noise analysis