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车辆中的声学事件检测:一种多标签分类方法
Anaswara Antony1,2, Wolfgang Theimer2, Giovanni Grossetti2
1Department of Computer Science, University of Applied Sciences and Arts (FH Dortmund), 44227 Dortmund, Germany.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究介绍了自动驾驶汽车的声学事件检测模型,通过添加"耳朵"来补充视觉传感器来提高安全性. 该模型在现实驾驶场景中准确识别各种声音.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 声学 声学 在声学方面
背景情况:
- 自动驾驶系统主要依赖于视觉传感器,如摄像头,雷达和激光雷达来感知环境.
- 整合审计信息可以显著提高无人驾驶汽车的可靠性和安全性.
- 目前的系统缺乏全面的基于音频的环境理解.
研究的目的:
- 开发和评估用于自动驾驶环境的声学事件检测 (AED) 模型.
- 通过检测声学事件及其时间来创建音频场景描述.
- 通过增强感官输入来提高自动驾驶汽车的安全性和稳定性.
主要方法:
- 利用从音频转换器 (BEAT) 网络中预先训练的双向编码器表示.
- 开发了一个单层神经网络,在各种各样的真实汽车音频录音数据库上进行训练.
- 使用各种参数和数据集评估模型性能,包括声音混合.
主要成果:
- 该模型实现了0.93的平均精度和0.39的F1-Score,用于11个声音类,具有0.5的信心值.
- 独立于值的平均精度达到0.77.
- 声音混合的表现仍然很强,平均精度,F1-Score和mAP分别为0.89,0.42和0.658.
结论:
- 拟议的AED模型有效地检测汽车环境中的声学事件,有助于更全面地了解场景.
- 音频感知与视觉数据的整合为更可靠,更安全的自动驾驶提供了有希望的途径.
- 该模型即使在重叠的声音事件中也表现出强大的性能,突出其实际应用性.
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