一个数据集用于自动驾驶汽车嵌入式系统中的环境声音识别
André Luiz Florentino1, Eva Laussac Diniz2, Plinio Thomaz Aquino-Jr3
1Centro Universitário FEI - Fundação Educacional Inaciana Pe. Saboia de Medeiros, Electric Engineering, São Bernardo do Campo, 09850-901, Brazil. andre.florentino@edag.com.br.
Scientific data
|July 5, 2025
概括
环境声音识别可以提高自动驾驶汽车的意识. 修改后的UrbanSound8K数据集 (US8K_AV) 增加了静音和背景类来支持这一发展.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动驾驶汽车需要超越视觉和触觉的先进传感系统.
- 环境声音识别可以模仿人类的听觉感知,以提高情境意识.
- 现有的数据集可能无法针对自动驾驶汽车的声音识别需求进行优化.
研究的目的:
- 为自动驾驶汽车应用适应UrbanSound8K数据集.
- 创建一个专门的数据集,用于训练自主系统的声音识别模型.
- 通过听觉线索,提高自动驾驶汽车的决策能力.
主要方法:
- 通过创建新的"背景"和"静音"类,扩展了UrbanSound8K数据集.
- 包含了来自Freesound.org的音频样本,用于"沉默"类.
- 在US8K_AV数据集的6个类别中注释了4,908个WAV文件,共计4.94小时.
主要成果:
- 开发了用于自动驾驶汽车的UrbanSound8K (US8K_AV) 数据集.
- US8K_AV数据集包含4.94小时的注释音频数据.
- 该数据集适合用于培训嵌入式系统的预测模型,如Raspberry Pi.
结论:
- 定制的US8K_AV数据集对于在自动驾驶汽车中推进环境声音识别至关重要.
- 这一数据集有助于开发更强大,更有意识的自动驾驶系统.
- 该研究支持集成听觉传感,以实现更安全,更有效的自主导航.
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