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A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

机译：一种机器学习方法用于发现爵士吉他音乐中表现性演奏动作的规则

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摘要

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.
机译：熟练的音乐家通过操纵声音属性（例如时间，能量，音调和音色）在他们的表演中引入表达。在这里，我们提出了一种数据驱动的计算方法，以针对爵士吉他音乐中的音符持续时间，发作，能量和装饰变换来诱导表达性能规则模型。我们从爵士吉他手格兰特·格林（Grant Green）的16组商业录音（和相应的乐谱）中提取高级功能，以表征作品中的表达。我们将机器学习技术应用于由此产生的功能，以学习表达性能规则模型。我们（1）定量评估归纳模型的准确性，（2）分析考虑的音乐特征的相对重要性，（3）在先前的工作中讨论一些学习到的表现力规则，以及（4）评估它们的表现力种姓。诱导的预测模型的准确性大大高于基线水平，这表明提取的音频表演和音乐特征包含足够的信息，可以自动学习信息丰富的表现形式。特征分析表明，预测表情变换最重要的音乐特征是音符持续时间，音高，音调强度，乐句位置，Narmour结构以及乐曲的节奏和调。发现诱导表达规则与文献报道的规则之间的异同。差异可能是由于以下事实：以前研究的大多数演奏数据都由古典音乐唱片组成。最后，通过将诱导规则应用于其他两位专业爵士吉他演奏者演奏的相同乐曲，来评估规则的演奏者特异性/一般性。结果表明，格兰特·格林（Grant Green）和其他两位音乐家之间的装饰方式一致，这可以解释为装饰规则通用性的良好指标。

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