Relational motif discovery via graph spectral ranking
Relational motif discovery via graph spectral ranking
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通过图谱排名发现关系主题
DOI：
10.1145/1830252.1830266
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发表时间：
2010
影响因子：
--
通讯作者：
A. Pinto
中科院分区：
文献类型：
--
作者：
A. Pinto
音乐摘要的目的是找到音乐作品(主题)中最具代表性的部分，以用于高效的音乐索引。在这里，我们提出了一种新的方法来发现音乐作品中的基调，基于图的谱排序。乐谱被分割成音乐片段的网络图，然后根据它们的中心性进行排名。可以采用不同的多音节和单音节概念来比较音乐片段。中心度越高的小节与音乐总结的相关性越大。我们对J·S·巴赫在多音节和单音节两部分发明中的语料库进行了评价。
Music summarization aims at finding the most representative parts of a music piece (motifs) that can be exploited for efficient music indexing. Here we present a novel approach for motif discovery in music pieces based on an graph spectral ranking. Scores are segmented into a network graph of music segments and then ranked depending on their centrality. Different poli- and mono-phonic metric concepts can be adopted to compare music segments. Bars with higher centrality are more relevant for music summarization. We present an evaluation on the corpus of J. S. Bach's 2-part Inventions both in poli- and mono-phonic configuration.