在城市音景中检测多声波事件任务的数据集:合成多声波环境声音源 (SPASS) 数据集
Rhoddy Viveros-Muñoz1, Pablo Huijse2,3, Victor Vargas1
1Instituto de Acústica, Universidad Austral de Chile, General Lagos 2086, Valdivia, Chile.
Data in brief
|September 25, 2023
概括
合成多声环境声音源 (SPASS) 数据集提供了25,000个合成音频片段,用于训练城市声音事件检测中的深度神经网络. 该资源有助于开发用于分析环境声音景观的先进系统.
科学领域:
- 声学和信号处理
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 在城市环境中,多声声响事件检测 (PSED) 对于环境监测至关重要.
- 现有的数据集可能无法充分反映城市声景的复杂性,用于训练深度学习模型.
- 福萨系统项目旨在通过人工神经网络 (ANN) 增强环境声音分析.
研究的目的:
- 为了引入合成多音波环境声音源 (SPASS) 数据集.
- 为PSED提供一个大规模的,公开可用的合成数据集,用于训练深度神经网络.
- 支持开发用于环境声音源检测和分类的智能系统.
主要方法:
- 使用RAVEN软件库生成合成多声音频数据.
- 单声声源的策划和五个虚拟环境 (公园,广场,街道,市场,海) 的配置.
- 创建25,000个音频片段 (每片10秒),并以44.1kHz的采样速率创建相关的元数据.
主要成果:
- SPASS数据集包括25,000个合成音频剪辑,每个虚拟环境有5,000个剪辑.
- 该数据集旨在有效地训练深度神经网络,以检测多声声音事件.
- 元数据包括各种城市声音景观中的层次声源分类.
结论:
- SPASS数据集是推动多声声响事件检测研究的宝贵资源.
- 这种合成数据集有助于开发强大的深度学习模型,用于分析复杂的城市音频环境.
- 该数据集有助于实现更广泛的目标,即创建用于环境声音分析的综合系统.
相关概念视频
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01:17Auditory Perception
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