scholarly journals A new optical music recognition system based on combined neural network

2015 ◽  
Vol 58 ◽  
pp. 1-7 ◽  
Author(s):  
Cuihong Wen ◽  
Ana Rebelo ◽  
Jing Zhang ◽  
Jaime Cardoso
Author(s):  
Zhe Xiao ◽  
Xin Chen ◽  
Li Zhou ◽  
◽  
◽  
...  

Traditional optical music recognition (OMR) is an important technology that automatically recognizes scanned paper music sheets. In this study, traditional OMR is combined with robotics, and a real-time OMR system for a dulcimer musical robot is proposed. This system gives the musical robot a stronger ability to perceive and understand music. The proposed OMR system can read music scores, and the recognized information is converted into a standard electronic music file for the dulcimer musical robot, thus achieving real-time performance. During the recognition steps, we treat note groups and isolated notes separately. Specially structured note groups are identified by primitive decomposition and structural analysis. The note groups are decomposed into three fundamental elements: note stem, note head, and note beams. Isolated music symbols are recognized based on shape model descriptors. We conduct tests on real pictures taken live by a camera. The tests show that the proposed method has a higher recognition rate.


Author(s):  
Graham Jones ◽  
Bee Ong ◽  
Ivan Bruno ◽  
Kia NG

This paper presents the applications and practices in the domain of music imaging for musical scores (music sheets and music manuscripts), which include music sheet digitisation, optical music recognition (OMR) and optical music restoration. With a general background of Optical Music Recognition (OMR), the paper discusses typical obstacles in this domain and reports currently available commercial OMR software. It reports hardware and software related to music imaging, discussed the SharpEye optical music recognition system and provides an evaluation of a number of OMR systems. Besides the main focus on the transformation from images of music scores to symbolic format, this paper also discusses optical music image restoration and the application of music imaging techniques for graphical preservation and potential applications for cross-media integration.


Author(s):  
YUNG-SHENG CHEN ◽  
FENG-SHENG CHEN ◽  
CHIN-HUNG TENG

Optical Music Recognition (OMR) is a technique for converting printed musical documents into computer readable formats. In this paper, we present a simple OMR system that can perform well for ordinary musical documents such as ballad and pop music. This system is constructed based on fundamental image processing and pattern recognition techniques, thus it is easy to implement. Moreover, this system has a strong capability in skew restoration and inverted musical score detection. From a series of experiments, the error for our skew restoration is below 0.2° for any possible document rotation and the accuracy of inverted musical score detection is up to 98.89%. The overall recognition accuracy of our OMR can achieve to nearly 97%, a figure comparable with current commercial OMR software. However, if taking into image skew into consideration, our system is superior to commercial software in terms of recognition accuracy.


Techno Com ◽  
2019 ◽  
Vol 18 (3) ◽  
pp. 214-226
Author(s):  
Dzikry Maulana Hakim ◽  
Ednawati Rainarli

Optical Music Recognition (OMR) adalah suatu cara untuk melakukan pengenalan pada notasi musik secara otomatis. Masalah utama dalam pendeteksian notasi musik adalah bagaimana sistem dapat mendeteksi sebuah notasi musik dan kemudian mengenali notasi musik tersebut. Notasi musik yang telah dikenali oleh mesin dapat dimanfaatkan untuk diproses kembali menjadi suara. Pada penelitian ini, proses segmentasi dilakukan untuk memotong setiap notasi. Untuk pengenalan notasi musik digunakan Convolutional Neural Network (CNN). Arsitektur CNN yang dipakai adalah kernel 3x3, jumlah layer pada feature learning sebanyak 3 convolutional layer dan 3 pooling layer, filter pada convolutional layer 64,128, 256 dan jumlah neuron pada hidden layer sebanyak 7168. Pengujian dilakukan dengan dua cara, yang pertama menguji performasi CNN menggunakan data notasi musik yang telah dipotong dan yang kedua adalah melakukan pengujian menggunakan sebaris notasi musik. Nilai akurasi yang didapatkan untuk pengenalan sebaris notasi musik tidak terlalu besar, yaitu 26,19%. Walaupun untuk proses segmentasi masih belum maksimal dalam memotong setiap notasi, namun metode CNN bekerja sangat baik untuk mengenali setiap notasi musik yang telah dipotong dengan benar. Hal ini ditunjukkan dari nilai akurasi yang mencapai 95,56%. 


2019 ◽  
Vol 10 (1) ◽  
pp. 135-140 ◽  
Author(s):  
Fei Yan

Abstract The main task of music recognition is to acquire relevant information of music content through processing and feature extraction of audio signals, and then used for comparison, classification, and automatic recording. The cognitive neural network based on T-S model is used to train the network weights with improved genetic algorithm in the paper. The strategy of membership function parameter adjustment is combined with the combination of momentum method and learning rate adaptive adjustment. The new proposed algorithm can be used in the music recognition algorithm by adding a compensation factor related to the input dimension on the membership degree, and the experimental result of the rule disaster caused by the excessive input dimension shows that the new proposed method can be applied to the music recognition system. At the same time, it shows that the accuracy rate of the recognition network is more accurate than that of the other algorithms, and its robustness is better.


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