This site uses cookies. By continuing to use this site you agree to our use of cookies. To find out more, see our Privacy and Cookies policy.
Paper The following article is Open access

Musical Instrument Recognition using Mel-Frequency Cepstral Coefficients and Learning Vector Quantization

and

Published under licence by IOP Publishing Ltd
, , Citation I Maliki and Sofiyanudin 2018 IOP Conf. Ser.: Mater. Sci. Eng. 407 012118 DOI 10.1088/1757-899X/407/1/012118

1757-899X/407/1/012118

Abstract

Musical instrument recognition is an essential subtask in many application regarding in music information retrieval. This research aims at extending the previous research saying that the MFCC used to feature extraction and LVQ method used to classification has good accuracy for musical instrument recognition. To test the methods described have been implemented in an android based application. Looking at the presented results, this research then focuses on to implementation that method for recognition musical instrument based on Aerophone, Electrophone, Chordophone, Idiophone, and Membranophone. The test was performed used 750 dataset with 5 sound source classes, the result of the performance test show that methods has 94.80% accuracy. It can be concluded that MFCC and LVQ methods can be implemented to recognize musical instruments.

Export citation and abstract BibTeX RIS

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Please wait… references are loading.