基于人工智能的声音景观分析:共同识别声音源并预测噪音的烦
Yuanbo Hou1, Qiaoqiao Ren2, Huizhong Zhang3
1Wireless, Acoustics, Environmental, and Expert Systems Research Group, Department of Information Technology, Ghent University, Gent, 9052, Belgium.
The Journal of the Acoustical Society of America
|November 15, 2023
概括
这项研究引入了一种用于声音景观分析的AI模型,准确地分类声音并预测人类烦. 该模型显示出对客观,长期的环境声音监测和评估的前景.
科学领域:
- 环境声学环境声学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 传统的音景研究依赖于用户调查,限制了长期监测和干预评估.
- 现有的声音信号方法往往侧重于精神声学指标或基本的声音识别,忽视主观评估.
- 人工智能驱动的方法需要客观地分析声音景观,包括声音识别和人类感知.
研究的目的:
- 提出和评估基于人工智能的双分支卷积神经网络,以交叉注意力为基础的融合 (DCNN-CaF) 进行全面的音景表征.
- 使用DeLTA数据集进行声音源分类 (SSC) 和人类感知的烦评级预测 (ARP).
- 证明模型能够捕捉声源和感知到的烦之间的关系.
主要方法:
- 开发了一个DCNN-CaF模型,整合了强度和Mel特征,以进行增强的音景分析.
- 使用了DeLTA数据集,其中包括人类注释的声音源标签和感知烦评级.
- 将DCNN-CaF模型与传统的机器学习方法和其他AI模型进行SSC和ARP任务的比较.
主要成果:
- 结合声量和Mel的DCNN-CaF模型具有超越性能的模型,只使用一个功能进行音景分析.
- 拟议的DCNN-CaF在SSC和ARP任务中表现出优异的性能,与现有的AI和传统机器学习方法相比.
- 相关性分析证实,人工智能模型对烦的预测与人类对声音源的感知一致.
- 一般化测试表明,即使在以前未遇到的声音源下,模型的可靠性能也很好.
结论:
- DCNN-CaF模型提供了一个强大的AI驱动的解决方案,用于客观的音景表征,包括声音识别和评估.
- 该模型预测人类烦的能力证明了其在环境监测和城市规划中的应用潜力.
- 这些发现支持使用人工智能来理解声音环境中的复杂的人与环境的相互作用.
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