A new optical music recognition system based on combined neural network

Wen, Cuihong; Rebelo, Ana; Zhang, Jing; Cardoso, Jaime S.

doi:10.1016/j.patrec.2015.02.002

Cited by 30 publications

(20 citation statements)

References 11 publications

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“…This is necessary to recognize chords or typical music artifacts such as dynamics. Finally, Wen et al [20] use connected components to segment symbols, which are later recognized using CNNs. This method is tested on both printed and handwritten scores.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

From Optical Music Recognition to Handwritten Music Recognition: A baseline

Baró

Riba

Calvo-Zaragoza

et al. 2019

Pattern Recognition Letters

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Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community.

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Section: Deep Learning-based Approachesmentioning

confidence: 99%

From Optical Music Recognition to Handwritten Music Recognition: A baseline

Baró

Riba

Calvo-Zaragoza

et al. 2019

Pattern Recognition Letters

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“…Get the connection weight from learning and then finish the appointed task, which can only be used in the associative mapping and its classification. After the orderly propagation through various layers, the input pattern is finally output in the output layer (Cuihong and Ana et al, 2015). Both the perceptron network and BP network belong to forward network, the structure of which is shown as Fig.3.…”

Section: Multi-layer Perceptron Model Of Neural Networkmentioning

confidence: 99%

Character Recognition Algorithm Based on Neural Network

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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Character recognition is a topic with great research value and it includes all typical issues in the field of pattern recognition: data acquisition, processing and selection, selection of input sample expression as well as the guided training on the recognizer with sample set. Artificial neural network (ANN) is a mathematical model which simulates the behavior characteristics of neural network and performs distributed and parallel information processing. Along with the progress in neural network and logic techniques, people have used many new methods and means on the study of character recognition. This paper proposes a character recognition method based on improved BP neural network. This method extracts features of training samples according to certain methods, builds the classification analysis system, divides each character to be recognized into 8*8 blocks for digitalization and represents each block with a vector. The input vector is defined as an input vector matrix and the vector represents a certain character or number. Then, it conducts feature extraction on certain unknown sample to be detected with the same method and the system will judge its classification according to the sample feature extracted. The training of neural network shall train the arrays which represent characters or numbers respectively in the input end under supervision and correspond to the specific location in the output end. The test result on this character recognition method shows that this algorithm has relatively high recognition accuracy, a faster processing speed and strong robustness in the applications.

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“…Commonly, OMR systems include a series of stages for image pre-processing [4,5], staff detection and removal [6,7], musical symbol isolation and recognition [8,9] and musical information reconstruction and representation [3,10].…”

Section: Introductionmentioning

confidence: 99%

“…Music information reconstruction includes the interpretation of the meaning of extracted musical symbols and, specifically, their relative position with respect to the corresponding staff. The detection and classification of musical symbols has been faced by using different approaches [11,12] with neural-network-based approaches coming out recently [8,13,14], which often require large datasets for training [15]. However, only recently some of these works specifically consider the full extraction of staff lines [16] or the precise identification of their location [17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Staff Reconstruction within SIMSSA Project

Tardón

Barbancho

et al. 2020

Applied Sciences

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The automatic analysis of scores has been a research topic of interest for the last few decades and still is since music databases that include musical scores are currently being created to make musical content available to the public, including scores of ancient music. For the correct analysis of music elements and their interpretation, the identification of staff lines is of key importance. In this paper, a scheme to post-process the output of a previous musical object identification system is described. This system allows the reconstruction by means of detection, tracking and interpolation of the staff lines of ancient scores from the digital Salzinnes Database. The scheme developed shows a remarkable performance on the specific task it was created for.

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A new optical music recognition system based on combined neural network

Cited by 30 publications

References 11 publications

From Optical Music Recognition to Handwritten Music Recognition: A baseline

From Optical Music Recognition to Handwritten Music Recognition: A baseline

Character Recognition Algorithm Based on Neural Network

Automatic Staff Reconstruction within SIMSSA Project

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