在健康的唱歌声中对自动语音模式的分类-一个XGBoost基于决策树的机器学习分类器
Jeroen Sol1, Mathias Aaen2, Cathrine Sadolin3
1Institute for Computing and Information Sciences, Radboud University, Nijmegen, the Netherlands.
Journal of voice : official journal of the Voice Foundation
|November 12, 2023
概括
机器学习模型可以自动分类唱歌的声音品质,比如金属和非金属声音. 虽然准确,但这些人工智能系统目前在语音模式歧视方面接近,但不超过人类专家的判断.
科学领域:
- 声学和音频信号处理
- 音乐表演中的人工智能
- 语音科学与技术 语音科学与技术
背景情况:
- 唱歌声音的听觉感知评估是标准的,但存在不一致的可靠性.
- 机器学习 (ML) 提供了客观,自动化语音分析的潜力.
- 以前的ML模型在对病态和健康声音的分类方面表现有前途.
研究的目的:
- 开发和评估XGBoost机器学习分类器,用于在健康的歌声中自动化声调分类.
- 将ML模型的性能与人类听觉感知评估进行比较.
- 为了确定关键的声学特征,以准确地分类歌唱的声音.
主要方法:
- 一个XGBoost决策树分类器被训练使用各种声学特征:Mel-frequency cepstrum系数 (MFCCs),状特征,语音质量特征和α比.
- 该模型被测试在区分金属与非金属歌唱和男性和女性歌手的一般声乐模式.
- 表演与41名专业歌手进行了比较,评估了64个声样.
主要成果:
- ML分类器在区分金属 (92%的F1分数为男性,87%为女性) 和声调 (70%的F1为男性,69%为女性) 方面取得了高准确性.
- 分类的关键特征是MFCC和alpha比率,仅使用这些模型的模型显示了可比性能.
- 自动化系统的性能接近或低于人类专家的评估.
结论:
- XGBoost模型显示了自动化歌唱声音分析的巨大潜力,特别是使用MFCC和alpha比.
- 当前的人工智能模型还不能与人类感知差异的准确性相匹配,但与之前的自动化方法相比,它是一种改进.
- 进一步的研究可能会增强人工智能的能力,以进行可靠和客观的歌声评估.
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