Optical Music Notes Recognition for Printed Music Score

Li, Chuanzhen; Zhao, Jiaqi; Cai, Jianfei; Wang, Hui; Du, Huaichang

doi:10.1109/iscid.2018.00071

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…As a past mainstream, multi-stage methods [16][17][18][19][20][21] used to decompose the task of OMR into multiple subproblems. The research of multi-stage methods mainly includes the steps of binarization [17], staff line removal [18,19], note detection, classification [20,21], and notation assembly [18]. Pinto et al [17] proposed a binarization based on domain knowledge.…”

Section: Related Workmentioning

confidence: 99%

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A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios

Liu,

Wu,

et al. 2023

Applied Sciences

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The recognition of printed music sheets in camera-based realistic scenarios is a novel research branch of optical music recognition (OMR). However, special factors in realistic scenarios, such as uneven lighting distribution and curvature of staff lines, can have adverse effects on OMR models designed for digital music scores. This paper proposes a stave-aware method based on object detection to recognize monophonic printed sheet music in camera-based scenarios. By detecting the positions of staff lines, we improve the accuracy of note pitch effectively. In addition, we present the Camera Printed Music Staves (CPMS) dataset, which consists of labels and images captured by mobile phones under different angles and lighting conditions in realistic scenarios. We compare our method after training on different datasets with a sequence recognition method called CRNN-CTC on the test set of the CPMS dataset. The results show that the accuracy, robustness, and data dependency of our method perform better.

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Section: Related Workmentioning

confidence: 99%

“…Szwoch et al [18] applied horizontal projection to remove staff lines. Chuanzhen Li et al [21] solved the problem of note head recognition and pitch position by adopting template matching. Szwoch et al [18] proposed a context-free attributed grammar for notation assembly.…”

Section: Related Workmentioning

confidence: 99%

A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios

Liu,

Wu,

et al. 2023

Applied Sciences

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Deep Neural Network Simulates Tempos Estimated by Five Wind Instrumentalists from Playing Sheet Music

KAWAMURA,

LIU,

MURAKAMI

et al. 2024

Transactions of Japan Society of Kansei Engineering

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Sound is generated by Kansei information processing performed by an instrumentalist playing a musical score. To construct datasets for Kansei information processing of musical scores, we have performed subjects' experiments using melodies extracted from some sets of music scores. All Japan Band Contests have a lot of pieces of music, and they are all selected as the required pieces, so we extracted the melodies from them and made a set of music scores for the subject experiment. The results of five subjects' experiments show that the subjects' estimated tempo differs. Furthermore, the tempo estimated by the subject from the score was defined as a 2-class classification problem of fast or slow tempo and simulated by a deep neural network. The recognition rate of the training data was more than 99.8% for all subjects' datasets, but the recognition rate of the evaluation data varied from 90.3% to 77.4%.

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Optical Music Notes Recognition for Printed Music Score

Cited by 2 publications

References 15 publications

A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios

A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios

Deep Neural Network Simulates Tempos Estimated by Five Wind Instrumentalists from Playing Sheet Music

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