Technical Reports - Query Results
Your query term was 'number = 2001-06'1 report found
- OFAI-TR-2001-06 (
181kB g-zipped PostScript file, 
652kB PDF file)Using AI and Machine Learning to Study Expressive Music Performance: Project Survey and First Report
- Gerhard Widmer
- This article presents a long-term inter-disciplinary research project
situated at the intersection of the scientific disciplines of Musicology
and Artificial Intelligence. The goal is to develop AI, and in particular
machine learning and data mining, methods to study the complex phenomenon
of expressive music performance.
Formulating formal, quantitative models of expressive performance is one of
the big open research problems in contemporary (empirical and cognitive)
musicology. Our project develops a new direction in this field:
we use inductive learning techniques to discover general and
valid expression principles from (large amounts of) real performance data.
The project is currently starting its third year and is planned to continue
for at least four more years.
In the following, we explain the basic notions of expressive music performance,
and why this is such a central phenomenon in music. We present the general
research framework of the project, and discuss the various challenges and
research opportunities that emerge in this framework. We then briefly
describe the current state of the project and list the main achievements
made so far. In the rest of the paper, we discuss in more detail one
particular data mining approach (including a new algorithm for learning
characterisation rules) that we have developed just recently.
Preliminary experimental results demonstrate that this algorithm can
discover very general and robust expression principles, some of which
actually constitute novel discoveries from a musicological viewpoint.
Keywords: Machine Learning, Data Mining, Expressive Music Performance
- This article presents a long-term inter-disciplinary research project
situated at the intersection of the scientific disciplines of Musicology
and Artificial Intelligence. The goal is to develop AI, and in particular
machine learning and data mining, methods to study the complex phenomenon
of expressive music performance.
Formulating formal, quantitative models of expressive performance is one of
the big open research problems in contemporary (empirical and cognitive)
musicology. Our project develops a new direction in this field:
we use inductive learning techniques to discover general and
valid expression principles from (large amounts of) real performance data.
The project is currently starting its third year and is planned to continue
for at least four more years.
In the following, we explain the basic notions of expressive music performance,
and why this is such a central phenomenon in music. We present the general
research framework of the project, and discuss the various challenges and
research opportunities that emerge in this framework. We then briefly
describe the current state of the project and list the main achievements
made so far. In the rest of the paper, we discuss in more detail one
particular data mining approach (including a new algorithm for learning
characterisation rules) that we have developed just recently.
Preliminary experimental results demonstrate that this algorithm can
discover very general and robust expression principles, some of which
actually constitute novel discoveries from a musicological viewpoint.