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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Eggink, J. Brown, G.J. |
| Copyright Year | 2004 |
| Description | Author affiliation: Dept. of Comput. Sci., Univ. of Sheffield, UK (Eggink, J.; Brown, G.J.) |
| Abstract | A system for musical instrument recognition is introduced. In contrast to most existing systems, it can identify a solo instrument even in the presence of an accompanying keyboard instrument or orchestra. To enable recognition in the presence of a highly polyphonic background, we use features based solely on the partials of the target tone. The approach is based on the assumption that it is possible to extract the most prominent fundamental frequency and the corresponding harmonic overtone series, and that these most often belong to the solo instrument. Classification is carried out using a Gaussian classifier trained on examples of monophonic music. Testing our system on accompanied sonatas and concertos we achieved a recognition rate of 86% for 5 different instruments, an accuracy comparable to that of systems limited to monophonic music only. |
| File Size | 248650 |
| File Format | |
| ISBN | 0780384849 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2004.1326802 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-05-17 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Instruments Frequency estimation Power system harmonics Pattern matching Computer science Convolution Acoustic noise Friction |
| Content Type | Text |
| Resource Type | Article |
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