Learning the Structure of Music

学习音乐的结构

• 批准号: EP/D062934/1
• 负责人: Eduardo Miranda
• 金额: $ 51.6万
• 依托单位: Plymouth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD062934%2F1
• 关键词: Learning; Structure; Music

This project is aimed at the development of models and tools for the application of novel probabilistic machine learning techniques to the analysis of music. The underlying theme of the project is the learning of patterns linking different data arising simultaneously from the same piece of music. The sources of data will be as follows: a) musical scores (MIDI format), b) audio (recordings of the pieces), c) worm data (charting performance information), d) EEG data (of subjects listening to the music) and d) fMRI data (of subjects listening to the music).The linking patterns that we will be seeking involve pairs of data streams as follows: a) musical scores with worm data, b) musical scores with fMRI data and c) audio with EEG data. The first pair will be used to identify typical performance patterns of particular performers. The second and the third pairs will be used to identify the effects in the brain of particular musical patterns (such as melodic sequences, musical phrasings, harmonic progressions, etc.).The project will advance our understanding of the relationship between musical structure and performance and experience. The potential of such developments is quite wide ranging, with potential application in music therapy and entertainment. For example, it will contribute to the development of systems for artificial performance of music imitating the style of a performer on pieces that he or she may have never played before and systems for musical composition tailored to achieve specific effects (or moods) on the listener.

该项目旨在开发新的概率机器学习技术应用于音乐分析的模型和工具。该项目的基本主题是学习从同一首音乐中同时产生的连接不同数据的模式。数据来源如下：a）乐谱（音频格式），B）音频（记录的片段），c）蠕虫数据（绘制性能信息），d）EEG数据（听音乐的受试者）和d）fMRI数据我们将寻求的链接模式涉及如下数据流对：a）具有蠕虫数据的乐谱，B）具有fMRI数据的乐谱和c）具有EEG数据的音频。第一对将用于确定特定表演者的典型表演模式。第二对和第三对将用于识别特定音乐模式（如旋律序列，音乐乐句，和声进行等）在大脑中的影响。该项目将促进我们对音乐结构与表演和体验之间关系的理解。这种发展的潜力是相当广泛的，在音乐治疗和娱乐的潜在应用。例如，它将有助于开发模仿表演者以前可能从未演奏过的作品的音乐风格的人工演奏系统，以及为实现听众的特定效果（或情绪）而定制的音乐创作系统。

项目成果

Constraint Programming Systems for Modeling Music Theories and Composition

• DOI: 10.1145/1978802.1978809
• 发表时间: 2011-10-01
• 期刊: ACM COMPUTING SURVEYS
• 影响因子: 16.6
• 作者: Anders, Torsten;Miranda, Eduardo R.
• 通讯作者: Miranda, Eduardo R.

Music Neurotechnology for Sound Synthesis: Sound Synthesis with Spiking Neuronal Networks

用于声音合成的音乐神经技术：使用尖峰神经元网络进行声音合成

• 发表时间: 2009
• 期刊: LEONARDO
• 影响因子: 0.3
• 作者: Miranda Eduardo R.

Constraint Application with Higher-Order Programming for Modeling Music Theories

音乐理论建模中高阶编程的约束应用

• DOI: 10.1162/comj.2010.34.2.25
• 发表时间: 2010
• 期刊: Computer Music Journal
• 作者: Anders T

Artificial Evolution of Expressive Performance of Music: An Imitative Multi-Agent Systems Approach

• DOI: 10.1162/comj.2010.34.1.80
• 发表时间: 2010-03
• 期刊: Computer Music Journal
• 作者: E. Miranda;Alexis Kirke;Qijun Zhang