优化音乐类型分类深度学习模型的配置
1Academy of Arts, Pingdingshan Polytenchnic College, Pingdingshan, 467000, Henan, China.
Heliyon
|February 2, 2024
概括
这项研究引入了一种新的深度学习方法,用于准确的音乐类型分类. 通过将Mel 频率 Cepstral 系数 (MFCC) 和短时间里埃转换 (STFT) 功能与优化的卷积神经网络 (CNN) 结合起来,该方法可以实现高精度.
科学领域:
- 音乐信息检索 音乐信息检索
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 音乐类型分类对于音乐检索至关重要.
- 手动的类型分类是耗时的.
- 现有的机器学习方法显示出与最佳性能差异.
研究的目的:
- 开发一种使用深度学习的新,准确的音乐类型预测方法.
- 改进现有的音乐类型分类方法.
主要方法:
- 信号预处理和特征提取,使用Mel频率面系数 (MFCC) 和短时间里叶变换 (STFT).
- 应用两个卷积神经网络 (CNN) 来单独分析MFCC和STFT特征.
- 对每个CNN模型进行超参数优化,使用黑洞优化 (BHO) 算法来最大限度地减少训练错误.
- 将两个CNN的输出与SoftMax分类器相结合,以最终确定类型.
主要成果:
- 拟议的方法实现了高分类准确性:GTZAN数据集上的95.2%,扩展舞厅数据集上的95.7%.
- 与之前的音乐类型分类工作相比,表现出优越的表现.
结论:
- 新的深度学习方法有效地对音乐类型进行了分类.
- 结合MFCC,STFT,优化的CNN和BHO,为自动音乐类型分类提供了一个强大的解决方案.
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