Applying supervised classifiers based on non-negative matrix factorization to musical instrument classification

Benetos, E., Kotti, M. & Kotropoulos, C. (2006). Applying supervised classifiers based on non-negative matrix factorization to musical instrument classification. Paper presented at the IEEE International Conference on Multimedia and Expo (ICME 2006), 9 - 12 July 2006, Toronto, Canada.

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Abstract

In this paper, a new approach for automatic audio classification using non-negative matrix factorization (NMF) is presented. Training is performed onto each audio class individually, whilst during the test phase each test recording is projected onto the several training matrices. Experiments demonstrating the efficiency of the proposed approach were performed for musical instrument classification. Several perceptual features as well as MPEG-7 descriptors were measured for 300 sound recordings consisting of 6 different musical instrument classes. Subsets of the feature set were selected using branch-and-bound search, in order to obtain the most discriminating features for classification. Several NMF techniques were utilized, namely the standard NMF method, the local NMF, and the sparse NMF. The experiments demonstrate an almost perfect classification (classification error 1.0%), outperforming the state-of-the-art techniques tested for the aforementioned experiment.

Item Type: Conference or Workshop Item (Paper)
Additional Information: DOI: 10.1109/ICME.2006.262650 © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Subjects: M Music and Books on Music > M Music
Q Science > QA Mathematics > QA76 Computer software
Divisions: School of Informatics > Department of Computing
URI: http://openaccess.city.ac.uk/id/eprint/2104

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