Name
Abstract | ||
|---|---|---|
Optical music recognition systems are in the general interest recently. These systems achieve accurate symbol recognition at some level. However, chords are not considered in these systems yet they play a role in music. Therefore, we aimed to develop an algorithm that can deal with separation and recognition of chords in music score images. Separation is necessary because the chords can be touched, overlapped or/and broken due to noise and other reasons. By considering these problems, we propose top-down based separation using domain information and characteristics of the chords. To handle recognition, we propose a modified zoning method with k-nearest neighbor classifier. Also, we analyzed several classifiers with different features to see which method is reliable for the chord recognition. Since this topic is not considered with special focus before, there is not a standard benchmark to evaluate performance of the algorithm. Thus, we introduce a new dataset, namely OMR-ChSR6306, which includes a wide range of chords such as single chords, touched chords, and overlapped chords. Experiments on the proposed dataset demonstrate that our algorithm can separate and recognize the chords, with 100% separation and 98.98% recognition accuracy respectively. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2557977.2558042 | ICUIMC |
Keywords | Field | DocType |
recognition accuracy,chord separation,optical music recognition system,printed music score image,music score image,accurate symbol recognition,new dataset,chord recognition,overlapped chord,proposed dataset,modified zoning method,different feature,chord,zoning,k nearest neighbor | Optical music recognition,k-nearest neighbors algorithm,Symbol recognition,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Chord (music),Classifier (linguistics),Harmony (color) | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elyor Kodirov | 1 | 144 | 8.15 |
| Sejin Han | 2 | 0 | 0.34 |
| Gueesang Lee | 3 | 208 | 52.71 |
| Youngchul Kim | 4 | 92 | 21.26 |