Optical Music Recognition Method Combining Multi-Scale Residual Convolutional Neural Network and Bi-Directional Simple Recurrent Units

Qiong, 吴琼 Wu; Qiang, 李锵 Li; Xin, 关欣 Guan

doi:10.3788/lop57.081006

Cited by 4 publications

(5 citation statements)

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“…In order to solve the problem of low recognition accuracy of music score images, Huang Zhiqing et al [6] developed an end-to-end target detection model to recognize music score notes based on Darknet53 base network and feature fusion technology extracts the feature map of music score images using the Darknet53 deep neural network and then spliced the upper feature map of the neural network with the feature map to complete the feature fusion so that the notes have a clearer feature texture, and then recognized them. In order to solve the problem of long recognition time of music score images, Wu Qiong et al [7 ] improved CNN to residual CNN based on the C-BiLSTM model of convolutional neural circulation network and formed the residual convolutional neural circulation network RC-BiLSTM and recognized the optical music score based on the note properties of the music score. Liang Mingheng [8] uses the depth network to identify musical notes, accepts the entire musical score image as input, and outputs the time value and pitch of the notes.…”

Section: Related Workmentioning

confidence: 99%

Classification of guitar tab and numbered musical notation using ResNet50 network

Ma¹,

Zhang²,

Han³

2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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With the advancement of deep learning and the growth of AI, a new concept for classifying music score images has been introduced. This paper presents a classification method for guitar tabs and numbered musical notation based on the ResNet50 network. The ResNet50 residual network model is used to extract features from music score images, and then the classification is performed based on the recognition probability. Results of the experiments indicate that the recognition accuracy of this model can reach 99.154%, suggesting that the ResNet50 network-based classification method holds good research potential in music score image classification.

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Section: Related Workmentioning

confidence: 99%

Classification of guitar tab and numbered musical notation using ResNet50 network

Ma¹,

Zhang²,

Han³

2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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“…The end-to-end method [2,[4][5][6]11,[32][33][34] has become the mainstream of OMR for its simplicity of data preprocessing. The vast majority of the end-to-end methods is sequence recognition, which means that it directly converts the stave images into symbol sequences with the pitch and type of each note.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the CRNN, Calvo-Zaragoza et al [2,3] proposed the CRNN-CTC by improving the network structure of CRNN and optimizing the mapping dictionary of musical symbol sequences, and achieved note type accuracy of 99.2% on printed dataset PrIMuS [3] and 96.6% on synthetically distorted dataset Camera-PrIMuS [11]. Qiong W et al [4] improved the CRNN-CTC by replacing the CNN with a multi-scale residual CNN and changing the BilSTM unit with BiSRU. It achieved an accuracy of 99.7% on the PrIMuS dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Over the past several decades, researchers have proposed various OMR methods, which typically focus on recognizing printed [2][3][4][5][6] or handwritten [7][8][9][10] music scores. However, most optical printed music recognition methods were designed for digitally printed scores rather than printed score photos in realistic scenarios [11][12][13].…”

Section: Introductionmentioning

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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“…With the continuous development of the field of computer vision, deep learning technology has also made continuous progress, and there are more and more studies using deep learning technology to solve the problem of optical score recognition [5][6][7] . Therefore, this paper proposes a musical note image recognition method based on the convolutional neural network model, and constructs a single-input multiple-output network model to recognize the musical note image.…”

Section: Introductionmentioning

confidence: 99%

Recognition of notational notation based on convolutional neural network

Fan

Chen²

2022

Second International Symposium on Computer Technology and Information Science (ISCTIS 2022)

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Aiming at the complex recognition process of numbered musical notes in traditional methods and the low recognition accuracy, a method for recognition of musical notes based on convolutional neural network is proposed. Based on the structural characteristics of the notational notes, a single-input and three-output note recognition network model is established. The convolutional neural network is trained using sample images containing note information labels to obtain a note image recognition model. The training results show that the accuracy of the model and the change trend of the loss value perform well. In order to test the practicability of this method, the numbered musical notes of some songs were recognized and compared with other methods. The results show that this method has high recognition accuracy and fast recognition speed, which proves the validity and practicality of the recognition model.

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Optical Music Recognition Method Combining Multi-Scale Residual Convolutional Neural Network and Bi-Directional Simple Recurrent Units

Cited by 4 publications

References 0 publications

Classification of guitar tab and numbered musical notation using ResNet50 network

Classification of guitar tab and numbered musical notation using ResNet50 network

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

Recognition of notational notation based on convolutional neural network

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