0


通过相似融合发现音乐结构

Discovering Music Structure via Similarity Fusion
课程网址: http://videolectures.net/mbc07_garcia_dms/  
主讲教师: Jerónimo Arenas-García
开课单位: 卡洛斯三世马德里大学
开课时间: 2008-02-01
课程语种: 英语
中文简介:
音乐导航和音乐推荐的自动方法利用音乐中的结构来对“歌曲空间”进行有意义的探索。为了从这样的系统中获得令人满意的性能,应该尽可能多地包含关于歌曲相似性的信息;但是,怎么做并不明显。在本文中,我们建立了已成功用于文档检索社区的概率潜在语义分析(PLSA)的思想。在这个概率框架下,任何歌曲都将被投射到相对低维度的“潜在语义”空间中,这样就可以使用潜在语义令人满意地解释所有观察到的相似性。因此,人们可以将这些语义视为音乐中的真实结构,因为它们可以解释观察到的歌曲之间的相似性。 PLSA模型表示音乐结构的适用性是在一个简化的场景中研究的,其中包括4412首歌曲和两个相似度量。结果表明PLSA模型是组合不同信息源的有用框架,并为歌曲表示提供了合理的空间。
课程简介: Automatic methods for music navigation and music recommendation exploit the structure in the music to carry out a meaningful exploration of the “song space”. To get a satisfactory performance from such systems, one should incorporate as much information about songs similarity as possible; however, how to do so is not obvious. In this paper, we build on the ideas of the Probabilistic Latent Semantic Analysis (PLSA) that have been successfully used in the document retrieval community. Under this probabilistic framework, any song will be projected into a relatively low dimensional space of “latent semantics”, in such a way that all observed similarities can be satisfactorily explained using the latent semantics. Therefore, one can think of these semantics as the real structure in music, in the sense that they can explain the observed similarities among songs. The suitability of the PLSA model for representing music structure is studied in a simplified scenario consisting of 4412 songs and two similarity measures among them. The results suggest that the PLSA model is a useful framework to combine different sources of information, and provides a reasonable space for song representation.
关 键 词: 音乐导航; 音乐推荐; 概率潜在语义分析
课程来源: 视频讲座网
最后编审: 2019-05-16:cjy
阅读次数: 80