TY - GEN
T1 - Musical instrument recognition on solo performances
AU - Essid, Slim
AU - Richard, Gael
AU - David, Bertrand
N1 - Publisher Copyright:
© 2004 EUSIPCO.
PY - 2015/4/3
Y1 - 2015/4/3
N2 - Musical instrument recognition is one of the important goals of musical signal indexing. If much effort has already been dedicated to the automatic recognition of musical instruments, most studies were based on limited amounts of data which often included only isolated notes. In this paper, two statistical approaches, namely the Gaussian Mixture Model (GMM) and the Support Vector Machines (SVM), are studied for the recognition of woodwind instruments using a large database of isolated notes and solo excerpts extracted from many different sources. Furthermore, it is shown that the use of Principal Component Analysis (PCA) to transform the feature data significantly increases the recognition accuracy. The recognition rates obtained range from 52.0 % for Bb Clarinet up to 96.0 % for Oboe.
AB - Musical instrument recognition is one of the important goals of musical signal indexing. If much effort has already been dedicated to the automatic recognition of musical instruments, most studies were based on limited amounts of data which often included only isolated notes. In this paper, two statistical approaches, namely the Gaussian Mixture Model (GMM) and the Support Vector Machines (SVM), are studied for the recognition of woodwind instruments using a large database of isolated notes and solo excerpts extracted from many different sources. Furthermore, it is shown that the use of Principal Component Analysis (PCA) to transform the feature data significantly increases the recognition accuracy. The recognition rates obtained range from 52.0 % for Bb Clarinet up to 96.0 % for Oboe.
M3 - Conference contribution
AN - SCOPUS:84979951013
T3 - European Signal Processing Conference
SP - 1289
EP - 1292
BT - 2004 12th European Signal Processing Conference, EUSIPCO 2004
PB - European Signal Processing Conference, EUSIPCO
T2 - 12th European Signal Processing Conference, EUSIPCO 2004
Y2 - 6 September 2004 through 10 September 2004
ER -