基于图形的音频分类使用预训练模型和图形神经网络
Andrés Eduardo Castro-Ospina1, Miguel Angel Solarte-Sanchez1, Laura Stella Vega-Escobar1
1Grupo de Investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
以图形形式表示音频数据显著改善了声音分类. 图形神经网络 (GNN) 显示出强的性能,图形注意网络 (GAT) 在环境声音和土地覆盖识别方面实现了高精度.
科学领域:
- 声学和信号处理
- 机器学习 机器学习
- 环境科学 环境科学
背景情况:
- 声音分类对于声学数据分析和环境监测至关重要.
- 传统方法可能无法完全捕捉复杂的音频模式.
- 图形表示为音频数据提供了一种新的方法.
研究的目的:
- 为了探索音频数据表示作为图表的声音分类.
- 评估各种图形神经网络 (GNN) 在音频任务上的性能.
- 确定环境声音分析中最有效的GNN模型.
主要方法:
- 利用预训练的音频模型来提取深度的音频功能.
- 使用提取的特征作为节点信息构建图形.
- 训练并比较图形卷积网络 (GCNs),图形SAGE和图形注意网络 (GATs).
主要成果:
- 音频数据的图形表示证明了对分类的有效性.
- 在声音分类任务中,GNN表现出了竞争力的表现.
- 图形注意网络 (GAT) 模型获得了最高的准确性:环境声音为83%,土地覆盖识别为91%.
结论:
- 图形表示学习是音频数据分析的一个有前途的技术.
- GNN,特别是GAT,为多类音频分类提供了一个强大的工具.
- 这种方法提高了环境环境中声学数据的解释和应用.
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