A machine learning approach to ornamentation modeling and synthesis in jazz guitar
A machine learning approach to ornamentation modeling and synthesis in jazz guitar
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DOI：
10.1080/17459737.2016.1207814
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发表时间：
2016-07-01
影响因子：
1.1
通讯作者：
Ramirez, Rafael
中科院分区：
文献类型：
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作者：
Giraldo, Sergio;Ramirez, Rafael
我们提出了一种机器学习的方法来自动生成表现力（mentiented）的爵士乐表演从非表现力的乐谱。从专业吉他手演奏的乐谱和相应的录音中提取的特征被用来训练用于预测旋律演奏的计算模型。作为第一步，研究了几种机器学习技术，以引入用于定时、起始和动态（即音符持续时间和能量）变换的回归模型，以及用于将音符分类为非重复或重复的重复表示模型。在第二步中，基于音符上下文相似性来选择最适合于预测的装饰音符。最后，使用拼接合成来自动合成新作品的表达性能，使用所诱导的模型。本文的补充在线材料包含自动生成的音乐片段的音乐示例，可以在doi：10.1080/17459737.2016.1207814和https://soundcloud.com/machine-learning-and-jazz上访问。在在线增刊中，我们展示了杰罗姆克恩的音乐作品《昨天》的一个例子，它是使用我们的方法学建模的，用于爵士吉他中富有表现力的音乐表演。
We present a machine learning approach to automatically generate expressive (ornamented) jazz performances from un-expressive music scores. Features extracted from the scores and the corresponding audio recordings performed by a professional guitarist were used to train computational models for predicting melody ornamentation. As a first step, several machine learning techniques were explored to induce regression models for timing, onset, and dynamics (i.e. note duration and energy) transformations, and an ornamentation model for classifying notes as ornamented or non-ornamented. In a second step, the most suitable ornament for predicted ornamented notes was selected based on note context similarity. Finally, concatenative synthesis was used to automatically synthesize expressive performances of new pieces using the induced models. Supplemental online material for this article containing musical examples of the automatically generated ornamented pieces can be accessed at doi: 10.1080/17459737.2016.1207814 and https://soundcloud.com/machine-learning-and-jazz. In the Online Supplement we present an example of the musical piece Yesterdays by Jerome Kern, which was modeled using our methodology for expressive music performance in jazz guitar.