在声乐艺术医学中利用机器学习:在歌剧中对"法奇"分类的随机森林应用
Zehui Wang1, Matthias Müller2, Felix Caffier3
1Institute for Digital Transformation, University of Applied Sciences Ravensburg-Weingarten, Doggenriedstraße, 88250 Weingarten, Germany.
Diagnostics (Basel, Switzerland)
|September 28, 2023
概括
确定歌剧歌手的声音类型 (Fach) 对声乐健康至关重要. 2004年对语音样本的机器学习分析在分类歌词与戏剧性声音方面取得了80%的准确性,有助于声乐艺术医学.
科学领域:
- 职业艺术 医学 医学
- 音乐表演科学 音乐表演科学
- 计算声学是一种计算声学.
背景情况:
- 专业声音障碍影响表演艺术家,特别是歌剧歌手.
- 错误的"法赫" (语音类型) 确定可能导致慢性过度使用和声损伤.
- 需要客观的专业咨询,以防止职业生涯终结的声音问题.
研究的目的:
- 为歌剧歌手开发一个客观的专业咨询方法.
- 利用数字声音分析和机器学习进行语音分类.
- 改进声乐艺术医学和歌唱教学中的诊断工具.
主要方法:
- 编制了2004年专业歌剧歌手声音样本的数据库.
- 采用随机森林算法,一个集体学习方法,用于专业分类.
- 在从语音样本中提取的声学特征上训练模型.
主要成果:
- 开发了一个高效的专业分类器,对歌词与戏剧性声音类型的准确性约为80%.
- 该系统成功地根据学习的声学特征对语音样本进行分类.
- 证明了机器学习在客观语音分析中的潜力.
结论:
- 开发的机器学习系统为歌剧歌手提供了改进的,客观的专业咨询.
- 这种方法可以帮助预防声损伤和过早终止职业生涯.
- 正在探索更多人工智能驱动的方法来增强声乐艺术医学诊断工具.
关键词:
数字声音分析 数字声音分析戏剧性的声音结构.情歌的声音结构.机器学习是机器学习.一个歌剧歌手歌剧演唱家.随机的森林随机的森林声乐艺术 医学 医学语音分类 语音分类 声音分类语音障碍预防 语音障碍预防语音音色参数 语音音色参数更多相关视频
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