密集的动态卷积网络用于贝尔坎托歌声技巧评估
Zhenyi Hou1, Xu Zhao2, Shanggerile Jiang2
1University of Shanghai for Science and Technology, Shanghai, 200093, China. hzy@usst.edu.cn.
Scientific reports
|May 5, 2025
概括
本研究引入了一个新的密集动态卷积网络 (DDNet),用于客观地评估贝尔坎托声乐技巧. 与传统模型相比,DDNet显著提高了评估歌唱表现的准确性.
科学领域:
- 音乐表演分析 音乐表演分析
- 在音频处理中使用人工智能
背景情况:
- 贝尔坎托表演评价是复杂的,需要精确量化声乐技术执行.
- 现有的卷积神经网络 (CNN) 在声乐表演中与复杂的光谱特征作斗争.
研究的目的:
- 开发一个客观的方法来评估贝尔·坎托的声乐技巧.
- 克服当前CNN在提取复杂的光谱特征以进行语音分析方面的局限性.
主要方法:
- 引入了全维动态卷积 (ODConv) 技术,用于增强光谱特征提取.
- 采用密集连接的层来优化多个尺度的功能利用.
- 开发了一个密集的动态卷积网络 (DDNet) 框架.
主要成果:
- 在声乐技术评估中,DDNet获得了90.11%的Top-1准确度.
- 在音乐分类和声乐场景分类方面表现优于传统的CNN和变压器模型.
- 在声音事件检测方面表现出高性能, mAP 41.89% .
结论:
- 拟议的DDNet显著提高了贝尔坎托声乐技术评估的准确性和效率.
- 该方法对声乐教学和远程音乐教育的应用有希望.
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