TY - GEN
T1 - Instrument identification in polyphonic music
T2 - 6th International Conference on Music Information Retrieval, ISMIR 2005
AU - Kitahara, Tetsuro
AU - Goto, Masataka
AU - Komatani, Kazunori
AU - Ogata, Tetsuya
AU - Okuno, Hiroshi G.
PY - 2005/12/1
Y1 - 2005/12/1
N2 - This paper addresses the problem of identifying musical instruments in polyphonic music. Musical instrument identification (MII) is an improtant task in music information retrieval because MII results make it possible to automatically retrieving certain types of music (e.g., piano sonata, string quartet). Only a few studies, however, have dealt with MII in polyphonic music. In MII in polyphonic music, there are three issues: feature variations caused by sound mixtures, the pitch dependency of timbres, and the use of musical context. For the first issue, templates of feature vectors representing timbres are extracted from not only isolated sounds but also sound mixtures. Because some features are not robust in the mixtures, features are weighted according to their robustness by using linear discriminant analysis. For the second issue, we use an F0-dependent multivariate normal distribution, which approximates the pitch dependency as a function of fundamental frequency. For the third issue, when the instrument of each note is identified, the a priori probablity of the note is calculated from the a posteriori probabilities of temporally neighboring notes. Experimental results showed that recognition rates were improved from 60.8% to 85.8% for trio music and from 65.5% to 91.1% for duo music.
AB - This paper addresses the problem of identifying musical instruments in polyphonic music. Musical instrument identification (MII) is an improtant task in music information retrieval because MII results make it possible to automatically retrieving certain types of music (e.g., piano sonata, string quartet). Only a few studies, however, have dealt with MII in polyphonic music. In MII in polyphonic music, there are three issues: feature variations caused by sound mixtures, the pitch dependency of timbres, and the use of musical context. For the first issue, templates of feature vectors representing timbres are extracted from not only isolated sounds but also sound mixtures. Because some features are not robust in the mixtures, features are weighted according to their robustness by using linear discriminant analysis. For the second issue, we use an F0-dependent multivariate normal distribution, which approximates the pitch dependency as a function of fundamental frequency. For the third issue, when the instrument of each note is identified, the a priori probablity of the note is calculated from the a posteriori probabilities of temporally neighboring notes. Experimental results showed that recognition rates were improved from 60.8% to 85.8% for trio music and from 65.5% to 91.1% for duo music.
KW - F0-dependent multivariate normal distribution
KW - MPEG-7
KW - Mixedsound template
KW - Musical context
KW - Musical instrument identification
UR - http://www.scopus.com/inward/record.url?scp=33846174759&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33846174759&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33846174759
SN - 9780955117909
T3 - ISMIR 2005 - 6th International Conference on Music Information Retrieval
SP - 558
EP - 563
BT - ISMIR 2005 - 6th International Conference on Music Information Retrieval
Y2 - 11 September 2005 through 15 September 2005
ER -