基于深度学习算法的音乐特征识别与分类

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Computational Intelligence and Applications Pub Date : 2023-04-06 DOI:10.1142/s1469026823500128
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引用次数: 0

摘要

本文对音乐特征识别与分类进行了研究。首先,分析了常见的信号特征,介绍了信号预处理方法。然后,提出了Mel–Phon系数(MPC)作为后续识别和分类的特征。应用深度信任网络(DBN)模型,并通过灰狼优化(GWO)算法进行改进,得到GWO–DBN模型。实验在GTZAN和免费音乐档案(FMA)数据集上进行。发现DBN的最佳隐层结构为1440-960-480-300。与决策树等机器学习方法相比,DBN模型在识别和分类音乐类型方面具有更好的分类性能。GWO–DBN模型的分类准确率达到75.67%。实验结果证明了GWO–DBN模型的可靠性。GWO–DBN模型可以在实际音乐研究中得到进一步的推广和应用。
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Music Feature Recognition and Classification Using a Deep Learning Algorithm
This paper studied music feature recognition and classification. First, the common signal features were analyzed, and the signal pre-processing method was introduced. Then, the Mel–Phon coefficient (MPC) was proposed as a feature for subsequent recognition and classification. The deep belief network (DBN) model was applied and improved by the gray wolf optimization (GWO) algorithm to get the GWO–DBN model. The experiments were conducted on GTZAN and free music archive (FMA) datasets. It was found that the best hidden-layer structure of DBN was 1440-960-480-300. Compared with machine learning methods such as decision trees, the DBN model had better classification performance in recognizing and classifying music types. The classification accuracy of the GWO–DBN model reached 75.67%. The experimental results demonstrate the reliability of the GWO–DBN model. The GWO–DBN model can be further promoted and applied in actual music research.
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
25
期刊介绍: The International Journal of Computational Intelligence and Applications, IJCIA, is a refereed journal dedicated to the theory and applications of computational intelligence (artificial neural networks, fuzzy systems, evolutionary computation and hybrid systems). The main goal of this journal is to provide the scientific community and industry with a vehicle whereby ideas using two or more conventional and computational intelligence based techniques could be discussed. The IJCIA welcomes original works in areas such as neural networks, fuzzy logic, evolutionary computation, pattern recognition, hybrid intelligent systems, symbolic machine learning, statistical models, image/audio/video compression and retrieval.
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