人工智能和深度神经网络下的民族歌剧中女性角色的歌唱风格
1Xingzhi College, Zhejiang Normal University, Jinhua, 321000, China. m13868991959@163.com.
Scientific reports
|June 27, 2025
概括
这项研究引入了一个AI模型来分类民族歌剧歌唱风格. 增强注意力的1D残余门卷积和双向循环神经网络 (ARGC-BRNN) 模型显著提高了歌唱风格分类的准确性.
科学领域:
- 音乐信息处理 音乐信息处理
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 在音乐信息处理中,分析音乐表演风格特征至关重要.
- 分类歌唱风格,特别是在民族歌剧中,带来了独特的挑战.
- 人工智能的进步需要高效的特征提取和分析技术.
研究的目的:
- 提出一种人工智能模型来对民族歌剧中的女性角色的歌唱风格进行分类.
- 开发一种能够有效地提取多层次歌唱风格特征的模型.
- 为了提高音乐表演风格分析的准确性和效率.
主要方法:
- 开发了一种注意力增强的1D残余门式卷积和双向循环神经网络 (ARGC-BRNN) 模型.
- 使用了带有挤压激发 (RGLU-SE) 的剩余门式线性单元块用于多层特征提取.
- 采用双向循环神经网络和用于时间依赖模型和全球特征聚合的注意力机制.
主要成果:
- ARGC-BRNN模型在一个自建的民族歌剧数据集上实现了0.872的准确性.
- 该模型在MagnaTagATune数据集上获得了0.912的曲线下的面积 (AUC).
- 与现有模型相比,证明了优越的分类性能和培训效率.
结论:
- ARGC-BRNN模型有效地捕捉了音乐的歌唱风格特征.
- 拟议的模型为民族歌剧艺术的数字和智能分析提供技术支持.
- 突出了AI在保存和分析传统音乐形式方面的潜力.
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