一个基于人工智能的分类器,用于媒体教育中的音乐情感表达
1Faculty of Creative Arts, Music Department, University of Malaya, Kuala Lumpur, Malaysia.
PeerJ. Computer science
|August 7, 2023
概括
这项研究引入了一种人工智能驱动的方法,用于使用音频功能进行音乐情感分析,达到95%以上的准确性. 这种方法通过准确地识别音乐中的快乐,悲伤,安静和兴奋等情绪,提高了教学效率.
科学领域:
- 人工智能的人工智能
- 音乐心理学 音乐心理学
- 教育技术的教育技术
背景情况:
- 音乐有效地传达情绪,调节情绪,这对学习至关重要.
- 目前的AI音乐情感分析通常依赖于歌词,忽视了音频信号处理.
- 人工智能对音乐信号的感知,传输和识别存在限制,用于情绪分析.
研究的目的:
- 利用音频功能开发基于AI的音乐情感分析方法.
- 通过在音乐教育中进行系统的情感评估,提高教学效率.
- 解决音乐情感识别中依赖歌词的AI模型的局限性.
主要方法:
- 使用声级处理和值确定进行智能分段和音符识别.
- 使用辐射基函数 (RBF) 模型构建音乐情感分类器.
- 训练分类器以相关反和与Hevner情绪模型进行比较.
主要成果:
- 在0.004分钟内准确识别音频特征.
- 在音乐情感分类方面实现了超过95%的准确率.
- 将情绪分为四类:安静,快乐,悲伤和兴奋.
结论:
- 基于音频特征的情绪分类与人类认知相一致.
- 开发的AI模型为音乐情感分析提供了更强大的方法.
- 这种方法有可能通过提高情感理解来增强音乐教育.
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