一个用于声音景观分类和保存的两级架构.
Samantha Di Loreto1, Sergio Montelpare2
1Engineering and Geology Department (INGEO), University "G. d'Annunzio" of Chieti-Pescara, Viale Pindaro 42, 65127, Pescara, Italy. samantha.diloreto@unich.it.
Scientific reports
|November 6, 2025
概括
本研究引入了一种新的开放式音频分类的两阶段系统,将深度学习与音景理论相结合. 该系统准确地对声音进行分类,验证理论框架,并使客观的音景分析成为可能.
科学领域:
- 音频信号处理 音频信号处理
- 深度学习是一种深度学习.
- 声学特征表示的表现.
背景情况:
- 声音分类对于环境监测和城市规划至关重要.
- 现有的方法可能无法完全捕捉到声音景观的细微差别.
- 马雷·谢弗 (R. Murray Schafer) 的声音景观理论为理解声学环境提供了一个框架.
研究的目的:
- 开发和评估一个新的两级系统,用于开放式音频分类.
- 将深度学习与舍弗的声音景观理论相结合,用于客观的声音景观分析.
- 通过使用定量指标来经验验证Schafer的理论框架.
主要方法:
- 使用变化自编码器 (VAE) 和卷积神经网络 (CNN) 的两级架构.
- 通过重建错误分析,VAE从mel光谱图中学习压缩的声学表示.
- 美国有线电视新闻网使用VAE学习的特征将声音分类为Schafer的类别 (关键音符,声音信号,声音标志).
主要成果:
- 在标准数据集 (UrbanSound8K,ESC-50,URBAN-SED,TUT城市声学场景) 中实现了平均准确度为80.7%.
- 在佩斯卡拉数据集上,计算分类和理论描述之间有很强的对齐.
- 成功量化了定性音景类别,从而实现了自动化分析.
结论:
- 拟议的系统客观地量化了音景元素,推进了音景分析.
- 这种方法论为舍弗的声音景观理论提供了经验验证.
- 允许自动分类,以保护音景和保护声学身份作为文化遗产.
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