Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity
Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity
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DOI：
10.1109/tmm.2014.2310701
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发表时间：
2014-08-01
影响因子：
7.3
通讯作者：
Arcos, Josep Ll
中科院分区：
文献类型：
--
作者：
Serra, Joan;Mueller, Meinard;Arcos, Josep Ll
自动推断原始多媒体文档的结构属性在当今数字化社会中是必不可少的。考虑到音乐作品的层次和多面组织，它对当前的计算系统构成了挑战。本文提出了一种基于结构特征与时间序列相似性相结合的音乐结构标注方法。结构特征封装了时间序列的局部和全局属性，并允许我们检测同质、新颖或重复片段之间的边界。时间序列相似度用于识别等效片段，对应于音乐上有意义的部分。对总共五个基准音乐集合和七种不同的人类注释进行的广泛测试表明，所提出的方法对不同的基础真值选择和参数设置具有鲁棒性。此外，我们看到它优于在相同框架下评估的先前方法。
Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework.