Efficient search with tree-edit distance for melody recognition.
The search of a given melody in large data-bases is one of the problems in the modern topic of music information retrieval (MIR). A huge amount of music files in symbolic formats can be found today in the Internet, and this has motivated new challenges for identification and categorization of music data. A number of pattern recognition techniques can be used to solve this problem. In this paper we explore the capabilities of trees to provide an expressive representation of music information. Trees are compared to string representations in terms of dissimilarity measures, using edit distances. The high computational cost of tree edit distances needs of complexity reduction techniques to be applied. Partial tree edit distances will be considered in order to solve this problem. The tree representation reduces significantly the error rates, and with partial tree edit distance the time requiered for classification is comparable to that of string representation.