基于深度秩的音乐序列转置不变距离
Deep rank-based transposition-invariant distances on musical sequences
摘要：
从音乐检索到自动音乐生成，各种应用都需要符号音乐序列上的距离。这些音乐序列属于一个给定的语料库（或风格），很明显，一个好的音乐序列距离应该考虑到这一信息;能够定义一个可以适用于所有音乐风格的距离似乎是不可信的。距离也可以在某些变换下是不变的，例如换位，因此它可以用作音乐动机之间的距离，而不是音乐序列之间的距离。然而，据我们所知，设计音乐距离的方法似乎都没有解决这些问题。本文介绍了一种方法来建立转置不变距离的符号音乐序列，从数据学习。它是一种混合距离，它结合了音乐序列的学习特征表示与手工制作的等级距离。与以前的方法相比，这种距离对数据的音乐编码的依赖性更小，并给出了感知上良好的结果。我们证明了它的有效性的数据集上的合唱旋律由J. S。巴赫.
英文摘要：
Distances on symbolic musical sequences are needed for a variety of applications, from music retrieval to automatic music generation. These musical sequences belong to a given corpus (or style) and it is obvious that a good distance on musical sequences should take this information into account; being able to define a distance ex nihilo which could be applicable to all music styles seems implausible. A distance could also be invariant under some transformations, such as transpositions, so that it can be used as a distance between musical motives rather than musical sequences. However, to our knowledge, none of the approaches to devise musical distances seem to address these issues. This paper introduces a method to build transposition-invariant distances on symbolic musical sequences which are learned from data. It is a hybrid distance which combines learned feature representations of musical sequences with a handcrafted rank distance. This distance depends less on the musical encoding of the data than previous methods and gives perceptually good results. We demonstrate its efficiency on the dataset of chorale melodies by J.S. Bach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
