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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tambouratzis, T. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Industrial Management and Technology, University of Piraeus, 107 Deligiorgi St, 185 34, Greece (Tambouratzis, T.) |
| Abstract | A novel optical music recognition (OMR) system is put forward, where the custom-made on-screen presentation of the music score (MS) is promoted via the recognition of key music symbols only. The proposed system does not require perfect manuscript alignment or noise removal. Following the segmentation of each MS page into systems and, subsequently, into staves, staff lines, measures and candidate music symbols (CMS's), music symbol recognition is limited to the identification of the clefs, accidentals and time signatures. Such an implementation entails significantly less computational effort than that required by classic OMR systems, without an observable compromise in the quality of the on-screen presentation of the MS. The identification of the music symbols of interest is performed via probabilistic neural networks (PNN's), which are trained on a small set of exemplars from the MS itself. The initial results are promising in terms of efficiency, identification accuracy and quality of viewing. |
| Starting Page | 1935 |
| Ending Page | 1942 |
| File Size | 638782 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033461 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Training Accuracy Noise Educational institutions Multiple signal classification Time measurement |
| Content Type | Text |
| Resource Type | Article |
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