Rigdelet神经网络和改进的部分增强效果优化器用于从声音频谱图像进行音乐类型分类
Fei Wang1, Shuai Fu2, Francis Abza3,4
1School of educational science, Jilin Normal College of Engineering Technology, Jilin, 130052, Jilin, China.
Heliyon
|August 6, 2024
概括
一种新的方法提高了音乐类型的分类使用增强的Rigdelet神经网络 (RNN) 优化与部分强化效果优化器 (IPREO). 这种方法在GTZAN数据集上达到92%的准确性,优于传统和深度学习模型.
科学领域:
- *音乐信息检索 *音乐信息检索
- * 机器学习 * 机器学习
- * * 信号处理 信号处理
背景情况:
- * 传统的音乐类型分类方法经常与复杂的音频数据作斗争.
- * 现有的深度学习模型显示出前景,但可能是计算密集型或易受局部最佳的.
研究的目的:
- *为音乐类型分类引入一种新且有效的方法.
- * 增强Rigdelet神经网络 (RNN) 的概括能力和性能,用于音频分析.
主要方法:
- * 将音频信号转换为声音频谱表示.
- *使用增强的Rigdelet神经网络 (RNN) 提取纹理特征.
- * 通过改进的部分增强效应优化器 (IPREO) 优化RNN,以避免局部优化.
主要成果:
- * 拟议的RNN/IPREO模型在GTZAN数据集上实现了92%的准确性.
- * 这显著优于K-Means (58%) 和支持矢量机 (高达68%).
- * 该模型还超过了各种深度学习架构,包括CNN (88%),VGG-16 (91%) 和ResNet-50 (90%).
结论:
- *混合CNN-双向RNN设计,加上IPREO优化,有效地提取复杂和顺序的听觉数据.
- *与最先进的深度学习模型相比,RNN/IPREO模型表现出具有竞争力的,有时甚至更优异的性能.
- *这种方法为准确的音乐类型分类提供了一个强大的工具.
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