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首页> 外文期刊>International Journal of Artificial Intelligence Tools: Architectures, Languages, Algorithms >A TOOL FOR GENERATING AND EXPLAINING EXPRESSIVE MUSIC PERFORMANCES OF MONOPHONIC JAZZ MELODIES
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A TOOL FOR GENERATING AND EXPLAINING EXPRESSIVE MUSIC PERFORMANCES OF MONOPHONIC JAZZ MELODIES

机译：产生和解释单声爵士乐曲表现音乐性能的工具

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摘要

In this paper we present a machine learning approach to modeling the knowledge applied by a musician when performing a score in order to produce an expressive performance of a piece. We describe a tool for both generating and explaining expressive music performances of monophonic Jazz melodies. The tool consists of three components: (a) a melodic transcription component which extracts a set of acoustic features from mono-phonic recordings, (b) a machine learning component which induce both an expressive transformation model and a set of expressive performance rules from the extracted acoustic features, and (c) a melody synthesis component which generates expressive mono-phonic output (MIDI or audio) from inexpressive melody descriptions using the induced expressive transformation model. We compare several machine learning techniques we have explored for inducing the expressive transformation model.
机译：在本文中，我们提出了一种机器学习方法，用于对音乐家进行乐谱时应用的知识进行建模，以产生乐曲的表现力。我们描述了一种用于生成和解释单音爵士旋律的表现音乐表现的工具。该工具包含三个组件：（a）旋律转录组件，可从单声道录音中提取一组声学特征;（b）机器学习组件，可从该组件中导出表达转换模型和一组表达性能规则提取的声学特征，以及（c）旋律合成组件，该组件使用诱导的表达转换模型从不表达旋律的描述中生成表达单声道输出（MIDI或音频）。我们比较了为探索表达变换模型而探索的几种机器学习技术。

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