Probabilistic models for melodic prediction
Probabilistic models for melodic prediction
复制标题
旋律预测的概率模型
DOI：
10.1016/j.artint.2009.06.001
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
D. Eck
中科院分区：
文献类型：
--
作者：
Jean;Samy Bengio;D. Eck
和弦进行是构成调性音乐的基石。在复调音乐中，和弦符号与实际音符序列之间的依赖关系的统计建模中，和弦的特定表示的选择有很大的影响。本文将旋律预测作为基准任务，使用来自输入/输出隐马尔可夫模型（iohmm）的两种概率模型架构来评估四种和弦表示的质量。可能性和条件和无条件预测错误率被用作每个提出的和弦表示质量的补充度量。根据经验，我们观察到不同的和弦表示是最优的，这取决于所选择的评价指标。此外，在大多数报道的实验中，仅用词根表示和弦似乎是一个很好的妥协。
Chord progressions are the building blocks from which tonal music is constructed. The choice of a particular representation for chords has a strong impact on statistical modeling of the dependence between chord symbols and the actual sequences of notes in polyphonic music. Melodic prediction is used in this paper as a benchmark task to evaluate the quality of four chord representations using two probabilistic model architectures derived from Input/Output Hidden Markov Models (IOHMMs). Likelihoods and conditional and unconditional prediction error rates are used as complementary measures of the quality of each of the proposed chord representations. We observe empirically that different chord representations are optimal depending on the chosen evaluation metric. Also, representing chords only by their roots appears to be a good compromise in most of the reported experiments.