| Paper: | AE-P4.3 | ||
| Session: | Applications to Music | ||
| Time: | Thursday, May 18, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Audio and Electroacoustics: Applications to Music | ||
| Title: | Linear Predictive Models for Musical Instrument Identification | ||
| Authors: | Nicolas Chetry, Mark Sandler, Queen Mary, University of London, United Kingdom | ||
| Abstract: | This paper deals with musical instrument identification. The proposed method consists of building the instrument models using a set of linear predictive coefficients, the Line Spectrum Frequencies. The models consist of characteristic short-term spectral envelopes calculated for each instrument in the database. The identification process involves the calculation of a similarity measure between two codebooks, one taken from the models database, one corresponding to the sample to identify. Next, the use of Support Vector Machines as classifier is investigated. The two systems are then applied to the identification of monophonic phrases extracted from commercial recordings. It is shown that good performance can be achieved for the classification of one unknown excerpt amongst 6 instruments. | ||
©2018 Conference Management Services, Inc. -||- email: webmaster@icassp2006.org -||- Last updated Friday, August 17, 2012