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Characteristics of Polyphonic Music Style and Markov Model of Pitch-Class Intervals

机译:音乐风格的特点和高级班级间隔的马尔可夫模型

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For the purpose of quantitatively characterising polyphonic music styles, we study computational analysis of some traditionally recognised harmonic and melodic features and their statistics. While a direct computational analysis is not easy due to the need for chord and key analysis, a method for statistical analysis is developed based on relations between these features and successions of pitch-class (pc) intervals extracted from polyphonic music data. With these relations, we can explain some patterns seen in the model parameters obtained from classical pieces and reduce a significant number of model parameters (110 to five) without heavy deterioration of accuracies of discriminating composers in and around the common practice period, showing the significance of the features. The method can be applied for polyphonic music style analyses for both typed score data and performed MIDI data, and can possibly improve the state-of-the-art music style classification algorithms.
机译:为了定量表征复态音乐风格,我们研究了一些传统公认的谐波和旋律特征及其统计数据的计算分析。虽然由于需要和弦和关键分析,直接计算分析并不容易,但是基于从多相音乐数据提取的这些特征与俯仰类(PC)间隔的关系之间的关系开发了一种统计分析方法。通过这些关系,我们可以解释在经典作品中获得的模型参数中看到的一些模式,并减少了大量的模型参数(110至5),而不会对常见实践期内和周围的鉴别作曲家的准确性劣化,显示出意义特征。该方法可以应用于两个键入的分数数据的复音音乐风格分析并执行MIDI数据,并且可以改善最先进的音乐风格分类算法。

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