基于深度神经网络的音乐旋律特征提取与识别

IF 0.7 Q4 ENGINEERING, MECHANICAL
Zhong‐Jiang Zhang
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引用次数: 0

摘要

音乐旋律可以用于区分音乐的流派风格,也可以用于检索音乐作品。本文使用了一种深度学习算法——卷积神经网络(CNN)来提取音乐旋律的特征并识别流派。在训练过程中使用了三元组样本作为训练样本。在模拟实验中,对算法中音乐片段的数量和激活函数的类型进行了正交实验。将CNN算法与支持向量机(SVM)和传统的CNN算法进行了比较。结果表明,不同音乐类型的音高和旋律曲线存在明显差异;当音乐片段数为6,激活函数为relu时,识别效果最好;由三元组样本训练的CNN算法具有更好的识别精度和更少的识别时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extraction and recognition of music melody features using a deep neural network
The music melody can be used to distinguish the genre style of music and can also be used for retrieving music works. This paper used a deep learning algorithm, the convolutional neural network (CNN), to extract the features of musical melodies and recognize genres. Three-tuple samples were used as training samples in the training process. Orthogonal experiments were conducted on the number of music segments and the type of activation function in the algorithm in the simulation experiments. The CNN algorithm was compared with support vector machine (SVM) and traditional CNN algorithms. The results showed that there were obvious differences in the pitch and melody curves of different genres of music; the recognition performance was best when the number of music segments was six and the activation function was relu; the CNN algorithm trained by three-tuple samples had better recognition accuracy and spent less recognition time.
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来源期刊
Journal of Vibroengineering
Journal of Vibroengineering 工程技术-工程:机械
CiteScore
1.70
自引率
0.00%
发文量
97
审稿时长
4.5 months
期刊介绍: Journal of VIBROENGINEERING (JVE) ISSN 1392-8716 is a prestigious peer reviewed International Journal specializing in theoretical and practical aspects of Vibration Engineering. It is indexed in ESCI and other major databases. Published every 1.5 months (8 times yearly), the journal attracts attention from the International Engineering Community.
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