论文标题
音乐的多重分析
Polytopic Analysis of Music
论文作者
论文摘要
音乐的结构分割是指找到歌曲组织的象征性表示的任务，从而将音乐流程减少到非重叠片段的分区。在此定义下，音乐结构可能不是唯一的，甚至可能是模棱两可的。解决这种歧义的一种方法是将此任务视为压缩过程，并将音乐结构视为给定压缩标准的优化。从这个角度来看，C。Guichaoua开发了一种基于“系统和对比度”模型的压缩驱动的模型，用于检索音乐结构，以及在多型的基础上，该模型是nhypercubes的扩展。我们介绍了这种模型，我们称之为“音乐的多重分析”，以及一个新的OpenSource专用工具箱，称为MusicOnpolyTopes（在Python中）。该模型还扩展到将Tonnetz用作关系系统的使用。结构分割实验是在RWC POP数据集上进行的。结果表明，与先前的C. guichaoua提出相比，改善了。
Structural segmentation of music refers to the task of finding a symbolic representation of the organisation of a song, reducing the musical flow to a partition of non-overlapping segments. Under this definition, the musical structure may not be unique, and may even be ambiguous. One way to resolve that ambiguity is to see this task as a compression process, and to consider the musical structure as the optimization of a given compression criteria. In that viewpoint, C. Guichaoua developed a compression-driven model for retrieving the musical structure, based on the "System and Contrast" model, and on polytopes, which are extension of nhypercubes. We present this model, which we call "polytopic analysis of music", along with a new opensource dedicated toolbox called MusicOnPolytopes (in Python). This model is also extended to the use of the Tonnetz as a relation system. Structural segmentation experiments are conducted on the RWC Pop dataset. Results show improvements compared to the previous ones, presented by C. Guichaoua.
