音乐诱导的情绪流模型由ENMI网络提供
Yunrui Shang1, Qi Peng2, Zixuan Wu2
1School of Automation, Qingdao University, Qingdao, China.
PloS one
|October 21, 2024
概括
本研究介绍了有效的Net-Music Informer (ENMI) 网络用于音乐情感识别,将音乐特征映射到情感维度. 恩米模型有效地预测情绪序列,在兴奋和价值维度上表现出强的表现.
科学领域:
- 计算音乐学 计算音乐学
- 情感计算是一种情感计算.
- 机器学习 机器学习
背景情况:
- 音乐深层次地唤起情绪的能力是众所周知的.
- 音乐情感识别 (MER) 试图将音乐特征与情感状态相关联.
- 现有的MER方法往往简化了对音乐的情绪反应的动态,连续性.
研究的目的:
- 将音乐情感映射概念化为一个多变量时间序列回归问题.
- 为了捕捉激发-情空间内的动态情绪流动.
- 引入和评估MER.效率的网音乐通报器 (ENMI) 网络.
主要方法:
- ENMI网络将Mel频谱特征与时间序列音乐数据集成在一起.
- 一个Music Informer模型被训练使用结合的功能来预测情绪序列.
- 在多个数据集上使用根平均平方误差 (RMSE) 评估模型的回归精度.
主要成果:
- 在DEAM数据集上获得了0.0440的激发和0.0352的价值的低RMSE值.
- 证明了该模型在DEAM,Emomusic和增强的Emomusic数据集中的有效性.
- 特征剥离和重要性分析证实了Mel谱图特征的贡献.
结论:
- 恩米网络提供了一种有效的方法来建模音乐诱导情绪的时间动态.
- 该研究强调了整合光谱和时间序列特征对于准确的MER的重要性.
- 拟议的模型为了解音乐的情感影响建立了一个强大的框架.
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