A Vocabulary-Free Infinity-Gram Model for Nonparametric Bayesian Chord Progression Analysis
A Vocabulary-Free Infinity-Gram Model for Nonparametric Bayesian Chord Progression Analysis
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发表时间：
2011
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通讯作者：
Kazuyoshi Yoshii;Masataka Goto
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作者：
Kazuyoshi Yoshii;Masataka Goto
本文提出了符号和弦序列的概率n-gram模型。为了克服传统模型中的基本限制，即模型最优性得不到保证，n的值是唯一固定的，以及和弦类型的词汇表（例如，major，minor，···）是任意定义的，我们提出了一个基于贝叶斯非参数化的无词汇的无限元语法模型.它接受任何音符的组合作为和弦类型，并允许每个和弦出现在一个序列中，有一个无限的和可变长度的上下文。当计算给定特定上下文的下一个和弦的预测概率时，考虑到n的所有可能性，并且当出现看不见的和弦类型时，我们可以通过自适应地评估0-gram概率来避免词汇外错误，即，音符成分的组合概率。我们使用披头士歌曲的实验表明，所提出的模型的预测性能优于国家的最先进的模型，我们可以找到随机连贯的和弦模式，通过排序可变长度的n-gram在一条线，根据其生成概率。
This paper presents probabilistic n-gram models for symbolic chord sequences. To overcome the fundamental limitations in conventional models—that the model optimality is not guaranteed, that the value of n is fixed uniquely, and that a vocabulary of chord types (e.g., major, minor, ··· )i s defined in an arbitrary way—we propose a vocabulary-free infinity-gram model based on Bayesian nonparametrics. It accepts any combinations of notes as chord types and allows each chord appearing in a sequence to have an unbounded and variable-length context. All possibilities of n are taken into account when calculating the predictive probability of a next chord given a particular context, and when an unseen chord type emerges we can avoid out-of-vocabulary error by adaptively evaluating the 0-gram probability, i.e., the combinatorial probability of note components. Our experiments using Beatles songs showed that the predictive performance of the proposed model is better than that of the state-of-theart models and that we could find stochastically-coherent chord patterns by sorting variable-length n-grams in a line according to their generative probabilities.