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Hearing01:31

Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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ESC-NAS:使用硬件意识的神经架构进行环境声音分类 寻找边缘

Dakshina Ranmal1, Piumini Ranasinghe1, Thivindu Paranayapa1

  • 1Department of Computer Science & Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.

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|June 27, 2024
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概括
此摘要是机器生成的。

本研究介绍ESC-NAS,一种硬件意识的方法,用于设计高效的深度学习模型,用于边缘设备上的环境声音分类. ESC-NAS优化了用于原始音频处理的神经架构,平衡精度和资源使用.

关键词:
深度学习是一种深度学习.环境声音分类环境声音分类硬件意识的神经架构搜索 硬件意识的神经架构搜索轻量级的卷积神经网络是一种轻量级的卷积神经网络.搜索空间 空间 搜索空间

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 深度学习和物联网集成对于智能解决方案至关重要,能够实现实时离线操作,提高准确性和减少资源需求.
  • 边缘设备上的环境声音分类 (ESC) 面临着由于有限的计算资源和需要处理原始音频数据的挑战.

研究的目的:

  • 提出ESC-NAS,一种新的硬件意识的神经架构搜索 (NAS) 方法,用于为ESC应用设计深度卷积神经网络 (CNN).
  • 开发针对原始音频输入优化的CNN架构,专注于尽量减少资源消耗,同时保持边缘部署的高精度.

主要方法:

  • 开发了一个基于单元的NAS搜索空间,包含2D卷积,批量规范化和最大聚合层,用于原始音频特征提取.
  • 采用黑盒贝叶斯优化搜索策略来探索NAS空间.
  • 评估模型架构使用硬件模拟来评估性能和资源消耗.

主要成果:

  • 与现有方法相比,ESC-NAS在模型性能和资源消耗之间实现了最佳的权衡.
  • 在基准数据集上实现了高精度:85.78% (FSC22),81.25% (UrbanSound8K),96.25% (ESC-10) 和81.0% (ESC-50).
  • 生成具有最佳尺寸和参数数量的模型,适合边缘部署.

结论:

  • ESC-NAS有效地设计了高效的深度学习模型,用于在资源有限的边缘设备上对环境声音进行分类.
  • 硬件意识的NAS方法可以创建专门的CNN,在处理原始音频数据方面表现出色.
  • 开发的模型为需要在边缘进行准确和高效的声音分析的现实应用提供了实用解决方案.