Conference Paper

Tree-Structured Representation of Musical Information.

In proceeding of: Pattern Recognition and Image Analysis, First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, June 4-6, 2003, Proceedings
Source: DBLP
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    Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings; 01/2010
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    ABSTRACT: Trees are a powerful data structure for representing data for which hierarchical relations can be defined. They have been applied in a number of fields like image analysis, natural language processing, protein structure, or music retrieval, to name a few. Procedures for comparing trees are very relevant in many task where tree representations are involved. The computation of these measures is usually a time consuming tasks and different authors have proposed algorithms that are able to compute them in a reasonable time, through approximated versions of the similarity measure. Other methods require that the trees are fully labelled for the distance to be computed. In this paper, a new measure is presented able to deal with trees labelled only at the leaves, that runs in O(|T A |×|T B |) time. Experiments and comparative results are provided. KeywordsTree edit distance-multimedia-music comparison and retrieval
    08/2010: pages 296-305;
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    ABSTRACT: This abstract describes the four methods presented by us with the objective of obtaining a good trade-off between accuracy and processing time [3]. Three of them are based on a summarization of the input musical data: the tree rep-resentation approach [5, 6] (UA T-RI2, and UA T3-RI3), and the quantized point-pattern representation [1] (UA PR -RI4). The fourth method is an ensemble of methods [4] (UA C-RI1). The summarization methods are expected to be faster than approaches dealing with raw representations of data. The ensemble combines different approaches try-ing to be more robust and are expected to give equal or bet-ter accuracy than the summarization methods. Thousands of different parametrizations of those methods are possi-ble. The parameters of the presented methods are chosen based on previous experiments.

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