Expressive variations of tempo and dynamics are an important aspect of music performances, involving a variety of underlying factors. Previous work has showed a relation between such expressive variations (in particular expressive tempo) and perceptual characteristics derived from the musical score, such as musical expectations, and perceived tension. In this work we use a computational approach to study the role of three measures of tonal tension proposed by Herremans and Chew (2016) in the prediction of expressive performances of classical piano music. These features capture tonal relationships of the music represented in Chew's spiral array model, a three dimensional representation of pitch classes, chords and keys constructed in such a way that spatial proximity represents close tonal relationships. We use non-linear sequential models (recurrent neural networks) to assess the contribution of these features to the prediction of expressive dynamics and expressive tempo using a dataset of Mozart piano sonatas performed by a professional concert pianist. Experiments of models trained with and without tonal tension features show that tonal tension helps predict change of tempo and dynamics more than absolute tempo and dynamics values. Furthermore, the improvement is stronger for dynamics than for tempo.
Towards An Integrated Approach for Expressive Piano Performance Synthesis from Music Scores
Queen Mary University of London（伦敦玛丽女王大学）；National Institute of Informatics（情报学研究所）；NICT（日本信息通信研究机构）；UKRI Centre for Doctoral Training in Artificial Intelligence and Music（英国研究理事会人工智能与音乐博士培训中心）；China Scholarship Council（中国留学基金委）
What were you expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music
Austrian Research Institute for Artificial Intelligence（奥地利人工智能研究所）；Department of Computational Perception, Johannes Kepler University Linz（林茨约翰内斯·开普勒大学计算感知系）
Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music
National Cheng Kung University（国立成功大学）；Academia Sinica（中央研究院）；International Audio Laboratories Erlangen（埃尔朗根国际音频实验室）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；University of Coimbra（科英布拉大学）；Taiwan AI Labs（台湾人工智能实验室）
自回归模型现在能够生成高质量的长达一分钟的表现力的钢琴演奏。尽管这一进展提出了新的工具来帮助音乐创作，但我们观察到，生成算法仍然没有被艺术家广泛使用，因为它们提供的控制有限，推理时间过长或音乐家工作流程中缺乏整合。在这项工作中，我们提出了钢琴修复应用程序（PIA），这是一个专注于修复钢琴表演的生成模型，因为我们相信这种基本操作（恢复钢琴表演的缺失部分）鼓励人机交互，并开辟了新的方法来处理音乐创作。我们的方法依赖于一个编码器-解码器线性Transformer架构训练的一种新的表示为钢琴演奏称为结构化的编码。通过揭示线性Transformers和我们的修复任务之间有趣的协同作用，我们能够有效地修复钢琴演奏的连续区域，这使得我们的模型适合交互式和响应式AI。辅助构图。最后，我们介绍我们的免费Ableton Live PIA插件，它允许音乐家在广泛使用的专业数字音频工作站内使用PIA顺利生成或修改任何视频剪辑。
A Manual Bar-by-Bar Tempo Measurement Protocol for Polyphonic Chamber Music Recordings: Design, Validation, and Application to Beethoven's Piano and Cello Sonatas
On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game
Austrian Research Institute for Artificial Intelligence（奥地利人工智能研究所）；Johannes Kepler University Linz（约翰内斯·开普勒大学林茨分校）；University of Tartu（塔尔图大学）；University of Oslo（奥斯陆大学）
A data-driven approach to mid-level perceptual musical feature modeling
Institute of Computational Perception（计算感知研究所）；Swiss Center for Affective Sciences（瑞士情感科学中心）；Johannes Kepler University（约翰内斯·开普勒大学）；Geneva University（日内瓦大学）
