Conference Paper

Tree-Structured Representation of Musical Information.

Conference: 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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    ABSTRACT: Evolutionary methods have been largely used in algorithmic music composition due to their ability to explore an immense space of possibilities. The main problem of genetic related composition algorithms has always been the implementation of the selection process. In this work, a pattern recognition-based system helped by a number of music analysis rules is designed for that task. The fitness value provided by this kind of supervisor (the music "critic") models the affect for a certain music genre after a training phase. The early stages of this work have been encouraging since they have responded to the a priori expectations and more work has to be carried out in the future to explore the creative capabilities of the proposed system.
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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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