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Virtual Music Teacher for New Music Learners with Optical Music Recognition

机译:具有光学音乐识别功能的面向新音乐学习者的虚拟音乐老师

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摘要

Learn to read and understand a music sheet, then play it on a musical instrument are difficult tasks to most beginner music learners. This motivates the authors to propose Virtual Music Teacher, a system to assist beginner music learners in their learning process. By applying our proposed lightweight Optical Music Recognition algorithm to scan and recognize a music sheet, then combine with sound classifying technique, the proposed system can learn what note to be played next, then help a music learner to play it correctly. The experimental results on the dataset consisting of 15 musical scores for beginners show that the proposed system can classify with precision up to 99.9 % using multiple SVM classifiers approach, whereas the sound classifying technique using Fast Fourier Transform can classify note's pitch recorded from a piano with precision up to 95.71 %. The system is implemented as an application on mobile devices and can be used to assist a music learner to play not only piano but other musical instruments as well.
机译:学习阅读和理解乐谱,然后在乐器上弹奏对大多数初学者来说都是艰巨的任务。这激发了作者提出虚拟音乐教师的建议,该系统可帮助初学者学习音乐的人学习。通过应用我们提出的轻量级光学音乐识别算法来扫描和识别乐谱,然后结合声音分类技术,该系统可以学习接下来要演奏的音符,然后帮助音乐学习者正确地演奏它。初学者15个乐谱组成的数据集上的实验结果表明,所提出的系统可以使用多个SVM分类器方法进行高达99.9%的精度分类,而使用快速傅里叶变换的声音分类技术可以对钢琴录制的音高进行分类。精度高达95.71%。该系统被实现为移动设备上的应用程序,并且可以用于帮助音乐学习者不仅演奏钢琴,而且还演奏其他乐器。

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