基于卷积神经网络的表示法识别

Zhaoyong Fan, Shuang Chen
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引用次数: 0

摘要

针对传统方法识别数字音符过程复杂、识别准确率低的问题,提出了一种基于卷积神经网络的音符识别方法。根据音符的结构特点,建立了单输入三输出的音符识别网络模型。利用包含音符信息标签的样本图像对卷积神经网络进行训练,得到音符图像识别模型。训练结果表明,该模型的精度和损失值的变化趋势表现良好。为了检验该方法的实用性,对部分歌曲的编号音符进行了识别,并与其他方法进行了比较。结果表明,该方法具有较高的识别精度和较快的识别速度,证明了该识别模型的有效性和实用性。
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Recognition of notational notation based on convolutional neural network
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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12
审稿时长
20 weeks
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