The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Data mining: concepts and techniques
Data mining: concepts and techniques
FlExPat: Flexible Extraction of Sequential Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
True suffix tree approach for discovering non-trivial repeating patterns in a music object
Multimedia Tools and Applications
Incrementally Mining Recently Repeating Patterns over Data Streams
New Frontiers in Applied Data Mining
A novel approach based on fault tolerance and recursive segmentation to query by humming
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
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Pattern extraction from music strings is an important problem. The patterns extracted from music strings can be used as features for music retrieval or analysis. Previous works on music pattern extraction only focus on exact repeating patterns. However, music segments with minor differences may sound similar. The concept of the prototypical melody has therefore been proposed to represent these similar music segments. In musicology, the number of music segments that are similar to a prototypical melody implies the importance degree of the prototypical melody to the music work. In this paper, a novel approach is developed to extract all the prototypical melodies in a music work. Our approach considers each music segment as a candidate for the prototypical melody and uses the edit distance to determine the set of music segments that are similar to this candidate. A lower bounding mechanism, which estimates the number of similar music segments for each candidate and prunes the impossible candidates is designed to speed up the process. Experiments are performed on a real data set and the results show a significant improvement of our approach over the existing approaches in the average response time.