歌唱能力评估:基于对象响应理论和歌唱数据的一般开源软件环境的歌唱测试的开发和验证
Sebastian Silas1,2, Daniel Müllensiefen3,4, Reinhard Kopiez4
1Goldsmiths University of London, London, UK. sebsilas@gmail.com.
Behavior research methods
|September 6, 2023
概括
我们开发了歌唱能力评估 (SAA),这是一个开源工具,可以使用物体响应理论来测量歌唱技能和旋律记忆. SAA提供了可靠的音调准确度和旋律回忆的得分,以及相关的R包,供更广泛的使用.
科学领域:
- 音乐认知 音乐认知
- 心理测量 心理测量 心理测量
- 计算审计处理计算审计处理
背景情况:
- 评估歌唱能力和旋律记忆在音乐认知和心理学中至关重要.
- 现有的方法往往缺乏标准化,开源工具,用于强大的在线和实验室评估.
- 了解歌唱表现的心理测量特性是一个正在进行的研究领域.
研究的目的:
- 开发和验证歌唱能力评估 (SAA),这是一个开源测试环境,用于测量人类歌唱能力和旋律记忆.
- 调查唱歌长音的基本维度和影响旋律回忆精度的因素.
- 为研究唱歌和听觉记忆的研究人员提供可访问的计算工具.
主要方法:
- 开发SAA开源测试环境,通过概率算法进行飞行音频转录.
- 应用潜变量方法和项目响应理论来评分歌唱表现.
- 进行两个在线实验 (N = 247;N = 910) 来收集唱歌数据并验证评估.
- 使用长音歌唱的主要组件分析和混合效果模型用于旋律回忆.
主要成果:
- 主要组件分析揭示了长音唱的三个维度:音调准确性,音调波动性和音调稳定性.
- 混合效果模型解释了旋律回忆表现的显著差异 (R2c = .42整体,R2c = .42节律,R2c = .38不节律).
- SAA的旋律分数与已建立的歌唱准确度指标,人口统计学变量和硬件配置有显著的关联.
结论:
- 歌唱能力评估 (SAA) 提供了可靠和有效的歌唱能力和旋律记忆的开源测量方法.
- 该研究确定了歌唱表现的关键维度和影响旋律回忆的因素.
- 发布了五个相关的R包,以促进研究环境中歌唱和旋律记忆测试的部署和分析.
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