A novel approach to the computer analysis of musical sound features has been made applying learning algorithms to the assessment of subjective scaling factors. A rough set theory recognized in artificial intelligence proven to be especially interesting in applications to acoustical assessments. Foundations of this theory and basic principles underlying the rough set algorithms are shown. Some multidimensional scaling methods of musical timbre are reviewed in order to provide data for the rough set computations. Correspondingly, examples of automatic classifications of sound features are obtained. Conclusions concerning the artificial intelligence approach to the processing of acoustic data are included.
Click to purchase paper as a non-member or you can login as an AES member to see more options.
No AES members have commented on this paper yet.
To be notified of new comments on this paper you can
subscribe to this RSS feed.
Forum users should login to see additional options.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.
