[1]刘彪,黄蓉蓉,林和,等.基于卷积神经网络的盲文音乐识别研究[J].智能系统学报,2019,14(01):186-193.[doi:10.11992/tis.201805002]
　LIU Biao,HUANG Rongrong,LIN He,et al.Research on braille music recognition based on convolutional neural networks[J].CAAI Transactions on Intelligent Systems,2019,14(01):186-193.[doi:10.11992/tis.201805002]
点击复制

基于卷积神经网络的盲文音乐识别研究(/HTML)
分享到：

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年01期
页码:
186-193
栏目:
学术论文—智能系统
出版日期:
2019-01-05

文章信息/Info

Title:
Research on braille music recognition based on convolutional neural networks
作者:
刘彪1; 2;  黄蓉蓉1;  林和1;  苏伟1
1. 兰州大学 信息科学与工程学院, 甘肃 兰州 730000;
2. 解放军69230部队, 新疆 乌苏 833000
Author(s):
LIU Biao1; 2;  HUANG Rongrong1;  LIN He1;  SU Wei1
1. School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China;
2. No.69230 Troops of PLA, Wusu 833000, China
关键词:
机器学习; 盲文音乐识别; 卷积神经网络; 深度学习; 计算机视觉; 图像识别; 人工智能; 图像处理
Keywords:
machine learning; braille music recognition; convolution neural network; deep learning; computer vision; image recognition; artificial intelligence; image processing
分类号:
TP39
DOI:
10.11992/tis.201805002
摘要:
盲人音乐家在交流创作的音乐作品时面临着人工转换和效率较低的问题，信息科学与技术的迅速发展为解决此类问题提供了许多解决方案。虽然目前有许多盲文音乐作品的识别方案，但其存在识别效率低和兼容能力不足等缺点。为了避免传统方案在盲文音乐图片特征提取时过多依赖人工经验，通过研究提出并设计了基于卷积神经网络的识别模型。在对盲文音乐图片的样例数据进行预处理之后，通过多次反复迭代训练，模型就可学习到盲文音乐图片中音乐符号的特征。实验结果表明，该模型的识别有效性和较强的泛化能力为盲文音乐作品的识别提供了一种新的解决方案。
Abstract:
Blind musicians are confronted with the problems of manual conversion and low efficiency in the communication of musical works. The rapid development of information science and technology has provided many solutions to these problems. However, most of the recognition schemes for braille music works lack recognition efficiency and compatibility. In consideration of this deficiency, whereby traditional schemes rely heavily on artificial experience in braille music picture extraction, a convolution neural network-based recognition model has been developed. After preprocessing the sample data of braille music pictures through repeated iterative training, the recognition model was able learn the characteristics of music notation in braille music pictures. The experimental results showed the recognition effectiveness and strong generalization ability of the model, which provides a new idea for the recognition of braille music works.

参考文献/References:

[1] MARIOTTI S P. Global data on visual impairments 2010[R]. Geneva:World Health Organization, 2012:1-17.
[2] ANTONACOPOULOS A, BRIDSON D. A robust braille recognition system[C]//6th International Workshop on Document Analysis Systems. Florence, Italy, 2004:533-545.
[3] MOUSA A, HIARY H, ALOMARI R, et al. Smart braille system recognizer[J]. IJCSI international journal of computer science issues, 2013, 10(6):52-60.
[4] SHREEKANTH T, UDAYASHANKARA V. An algorithmic approach for double sided Braille dot recognition using image processing techniques[J]. International journal of image processing and visual communication, 2014, 2(4):1-6.
[5] 王倩倩. 基于DSP的简谱识别和盲文乐谱转换算法研究与实现[D]. 沈阳:东北大学, 2013. WANG Qianqian. Research and implementation of numbered musical notation identification and braille musical translation algorithm based on DSP[D]. Shenyang:Northeastern University, 2013.
[6] 黄河燕, 周强, 沈鑫, 等. 基于对节记谱法的盲文乐谱自动排版方法及装置:CN105719626A[P]. 2016-06-29. HUANG Heyan, ZHOU Qiang, SHEN Xin, et al. Automatic braille music score typesetting method and device based on by-rhythm stave memorizing method:CN105719626A[P]. 2016-06-29.
[7] 张志强. 视障乐谱转换软件SunMusic的设计实现[D]. 兰州:兰州大学, 2016. ZHANG Zhiqiang. The design and implementation of music conversion software SunMusic for visually impaired[D]. Lanzhou:Lanzhou University, 2016.
[8] 李婷. 基于深度学习的盲文识别方法[J]. 计算机与现代化, 2015(6):37-40 LI Ting. A deep learning method for braille recognition[J]. Computer and modernization, 2015(6):37-40
[9] ALI H S, ASSABIE Y. Recognition of double sided Amharic braille documents[J]. International journal of image, graphics and signal processing, 2017, 9(4):1-9.
[10] JIBRIL E C, MESHESHA M. Recognition of Amharic braille documents[C]//The 5th International Conference on the Advancement of Science and Technology. 2017, 15:22-29.
[11] WALEED M. Braille identification system using artificial neural networks[J]. Tikrit journal of pure science, 2017, 22(2):140-145.
[12] AL NASSIRI A, ABDULLA S. A fuzzy classification and recognition system for arabic braille segmented characters[J]. International journal of applied engineering research, 2018, 13(6):3662-3669.
[13] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, 2012:1097-1105.
[14] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
[15] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, 2016:770-778.
[16] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6):1229-1251 ZHOU Feiyan, JIN Linpeng, DONG Jun. Review of convolutional neural network[J]. Chinese journal of computers, 2017, 40(6):1229-1251
[17] 常亮, 邓小明, 周明全, 等. 图像理解中的卷积神经网络[J]. 自动化学报, 2016, 42(9):1300-1312 CHANG Liang, DENG Xiaoming, ZHOU Mingquan, et al. Convolutional neural networks in image understanding[J]. Acta automatica sinica, 2016, 42(9):1300-1312
[18] 李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016, 36(9):2508-2515, 2565 LI Yandong, HAO Zongbo, LEI Hang. Survey of convolutional neural network[J]. Journal of computer applications, 2016, 36(9):2508-2515, 2565
[19] 周俊宇, 赵艳明. 卷积神经网络在图像分类和目标检测应用综述[J]. 计算机工程与应用, 2017, 53(13):34-41 ZHOU Junyu, ZHAO Yanming. Application of convolution neural network in image classification and object detection[J]. Computer engineering and applications, 2017, 53(13):34-41
[20] GU Jiuxiang, WANG Zhenhua, KUEN J, et al. Recent advances in convolutional neural networks[J]. Pattern recognition, 2018, 77:354-377.
[21] LI Yangyan, SU Hao, QI C R, et al. Joint embeddings of shapes and images via CNN image purification[J]. ACM transactions on graphics (TOG), 2015, 34(6):1-5.
[22] 卢宏涛, 张秦川. 深度卷积神经网络在计算机视觉中的应用研究综述[J]. 数据采集与处理, 2016, 31(1):1-17 LU Hongtao, ZHANG Qinchuan. Applications of deep convolutional neural network in computer vision[J]. Journal of data acquisition and processing, 2016, 31(1):1-17
[23] 李念峰, 董迎红, 肖志国. 基于图像处理的盲文自动识别系统研究[J]. 制造业自动化, 2012, 34(3):63-67 LI Nianfeng, DONG Yinghong, XIAO Zhiguo. Study on image processing based braille automatic identification system[J]. Manufacturing automation, 2012, 34(3):63-67

相似文献/References:

[1]叶志飞,文益民,吕宝粮.不平衡分类问题研究综述[J].智能系统学报,2009,4(02):148.
　YE Zhi-fei,WEN Yi-min,LU Bao-liang.A survey of imbalanced pattern classification problems[J].CAAI Transactions on Intelligent Systems,2009,4(01):148.
[2]刘奕群,张 敏,马少平.基于非内容信息的网络关键资源有效定位[J].智能系统学报,2007,2(01):45.
　LIU Yi-qun,ZHANG Min,MA Shao-ping.Web key resource page selection based on non-content inf o rmation[J].CAAI Transactions on Intelligent Systems,2007,2(01):45.
[3]马世龙,眭跃飞,许 可.优先归纳逻辑程序的极限行为[J].智能系统学报,2007,2(04):9.
　MA Shi-long,SUI Yue-fei,XU Ke.Limit behavior of prioritized inductive logic programs[J].CAAI Transactions on Intelligent Systems,2007,2(01):9.
[4]姚伏天,钱沄涛.高斯过程及其在高光谱图像分类中的应用[J].智能系统学报,2011,6(05):396.
　YAO Futian,QIAN Yuntao.Gaussian process and its applications in hyperspectral image classification[J].CAAI Transactions on Intelligent Systems,2011,6(01):396.
[5]文益民,强保华,范志刚.概念漂移数据流分类研究综述[J].智能系统学报,2013,8(02):95.[doi:10.3969/j.issn.1673-4785.201208012]
　WEN Yimin,QIANG Baohua,FAN Zhigang.A survey of the classification of data streams with concept drift[J].CAAI Transactions on Intelligent Systems,2013,8(01):95.[doi:10.3969/j.issn.1673-4785.201208012]
[6]杨成东,邓廷权.综合属性选择和删除的属性约简方法[J].智能系统学报,2013,8(02):183.[doi:10.3969/j.issn.1673-4785.201209056]
　YANG Chengdong,DENG Tingquan.An approach to attribute reduction combining attribute selection and deletion[J].CAAI Transactions on Intelligent Systems,2013,8(01):183.[doi:10.3969/j.issn.1673-4785.201209056]
[7]胡小生,钟勇.基于加权聚类质心的SVM不平衡分类方法[J].智能系统学报,2013,8(03):261.
　HU Xiaosheng,ZHONG Yong.Support vector machine imbalanced data classification based on weighted clustering centroid[J].CAAI Transactions on Intelligent Systems,2013,8(01):261.
[8]丁科,谭营.GPU通用计算及其在计算智能领域的应用[J].智能系统学报,2015,10(01):1.[doi:10.3969/j.issn.1673-4785.201403072]
　DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10(01):1.[doi:10.3969/j.issn.1673-4785.201403072]
[9]孔庆超,毛文吉,张育浩.社交网站中用户评论行为预测[J].智能系统学报,2015,10(03):349.[doi:10.3969/j.issn.1673-4785.201403019]
　KONG Qingchao,MAO Wenji,ZHANG Yuhao.User comment behavior prediction in social networking sites[J].CAAI Transactions on Intelligent Systems,2015,10(01):349.[doi:10.3969/j.issn.1673-4785.201403019]
[10]姚霖,刘轶,李鑫鑫,等.词边界字向量的中文命名实体识别[J].智能系统学报,2016,11(1):37.[doi:10.11992/tis.201507065]
　YAO Lin,LIU Yi,LI Xinxin,et al.Chinese named entity recognition via word boundarybased character embedding[J].CAAI Transactions on Intelligent Systems,2016,11(01):37.[doi:10.11992/tis.201507065]

备注/Memo

备注/Memo:
收稿日期:2018-05-04。
基金项目:广西科技计划项目（桂科AA17204096，桂科AD16380076）；兰州市人才创新创业科技项目（2014-RC-3）.
作者简介:刘彪,男,1984年生,硕士研究生,主要研究方向为智能软件与机器学习;黄蓉蓉,女,1994年生,硕士研究生,主要研究方向为人工智能与机器学习;林和,男,1963年生,副教授,主要研究方向为人工智能与机器学习。发表学术论文100余篇。
通讯作者:林和.E-mail:linhe@lzu.edu.cn
更新日期/Last Update: 1900-01-01