共同学习情感和歌唱风格,以增强音乐风格的理解
Yuwen Chen1, Jing Mao1, Rui-Feng Wang2
1School of Humanities and Arts, Hunan Institute of Traffic Engineering, Hengyang 421219, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了一种多任务学习框架,用于联合唱歌风格和情绪分类. 这种新的方法通过在任务之间分享知识来提高准确性,优于独立模型.
科学领域:
- 音乐信息检索 音乐信息检索
- 机器学习 机器学习
- 声乐表现分析 声乐表现分析
背景情况:
- 了解音乐风格对于个性化推等应用程序至关重要.
- 现有的研究往往将情绪和歌唱风格的分类视为单独的任务.
- 声乐情绪和歌唱风格之间的内在关系经常被忽视.
研究的目的:
- 开发一个多任务学习框架,用于联合情绪和歌唱风格的分类.
- 为了使两项任务之间明确的知识共享和相互增强.
- 为了提高歌唱风格分析的稳定性和准确性.
主要方法:
- 实施了多任务学习框架,共同模拟情绪和歌唱风格分类.
- 在各种骨干架构 (Transformer,TextCNN,BERT) 中评估了框架.
- 使用专业记录设备对自建基准数据集进行实验.
主要成果:
- 联合优化始终优于单任务学习方法.
- 该框架在不同的骨干架构中显示出稳定的性能改进.
- 在基准数据集中的两个分类任务中实现了最先进的准确性.
- 提供了对情绪表达和声乐风格之间的相互作用的可解释的见解.
结论:
- 共同建模歌唱风格和情绪分类可以提高分析强度.
- 拟议的多任务框架可以在各种架构中进行概括和适应.
- 这种方法可以更全面地了解声乐表现特征.
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