Technical Reports - Query Results
Your query term was 'number = 2004-04'1 report found
- ÖFAI-TR-2004-04 (
180kB g-zipped PostScript file, 
280kB PDF file)Automatic Recognition of Famous Artists by Machine
- Gerhard Widmer, Patrick Zanon
- The paper addresses the question whether it is possible for a machine to learn
to distinguish and recognise famous musicians (concert pianists), based
on their style of playing. We extract a number of low-level
features related to expressive timing and dynamics from the original audio CD
recordings by famous pianists, and apply various machine learning algorithms
to the task of learning classifiers based on these features. Experiments show
that the computer can learn to identify the performer in a new recording with a
probability significantly higher than chance, despite the fact that the
features only capture a very limited amount of information about a performance.
An analysis of the learned classifiers reveals a number of performance features
that seem particularly relevant to style differentiation, and an application
of the classifiers to music of a very different style shows
that the machine seems to have captured truly fundamental aspects of
artistic style. One limitation of the current approach is that sequential
information is totally ignored, and we briefly report
on ongoing work that tries to address this problem via an interesting
conversion of music performances to strings.
Keywords: machine learning, music
- The paper addresses the question whether it is possible for a machine to learn
to distinguish and recognise famous musicians (concert pianists), based
on their style of playing. We extract a number of low-level
features related to expressive timing and dynamics from the original audio CD
recordings by famous pianists, and apply various machine learning algorithms
to the task of learning classifiers based on these features. Experiments show
that the computer can learn to identify the performer in a new recording with a
probability significantly higher than chance, despite the fact that the
features only capture a very limited amount of information about a performance.
An analysis of the learned classifiers reveals a number of performance features
that seem particularly relevant to style differentiation, and an application
of the classifiers to music of a very different style shows
that the machine seems to have captured truly fundamental aspects of
artistic style. One limitation of the current approach is that sequential
information is totally ignored, and we briefly report
on ongoing work that tries to address this problem via an interesting
conversion of music performances to strings.