Abstract
This paper presents a neural network approach for classification of musical instrument sounds through Radial and Elliptical Basic Functions. In particular, we discuss a novel automatic network fine-tuning method called Nearest Centroid Error Clustering (NCC) which determines a robust number of centroids for improved system performance. 829 monophonic sound examples from the string, brass, and woodwind families were used. A number of different performance techniques, dynamics, and pitches were utilized in training and testing the system resulting in 71% correct individual instrument classification (12 classes) and 88% correct instrument family (3 classes) classification.
Original language | English (US) |
---|---|
Title of host publication | International Computer Music Conference, ICMC 2005 |
Publisher | International Computer Music Association |
State | Published - 2005 |
Event | International Computer Music Conference, ICMC 2005 - Barcelona, Spain Duration: Sep 5 2005 → Sep 9 2005 |
Other
Other | International Computer Music Conference, ICMC 2005 |
---|---|
Country/Territory | Spain |
City | Barcelona |
Period | 9/5/05 → 9/9/05 |
ASJC Scopus subject areas
- Computer Science Applications
- Media Technology
- Music