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相关概念视频

Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

188
The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
188

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相关实验视频

Updated: May 28, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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使用希尔伯特变换音频事件编码方案进行低功耗尖端神经网络音频源本地化.

Saeid Haghighatshoar1, Dylan Richard Muir2

  • 1SynSense, Zürich, Switzerland.

Communications engineering
|February 11, 2025
PubMed
概括

本研究介绍了一种高效的声音源本地化方法,使用希尔伯特变换和基于事件的编码来对超低功率尖端神经网络 (SNN) 进行编码. 这种方法可以实现高精度,同时显著降低物联网设备的功耗.

科学领域:

  • 信号处理 信号处理
  • 计算神经科学是一种神经科学.
  • 声学 声学 在声学方面

背景情况:

  • 音源定位对于消费电子产品的降噪至关重要.
  • 传统的光束成形方法需要密集的过器,并且是计算密集的,限制了它们在低功耗设备中的使用.
  • 尖端神经网络 (SNN) 为高效的音频处理提供了潜力,但需要专门的算法.

研究的目的:

  • 开发一种高效的声音源定位方法,适用于超低功率尖端神经网络 (SNN).
  • 为了减少传统光束成形技术的计算需求.
  • 为了使功率受限制的物联网 (IoT) 设备中高精度的音频源分离.

主要方法:

  • 使用希尔伯特变换来处理宽带音频而不需要密集的带通波器.
  • 开发了一种基于事件的编码方法,以捕获复杂的分析信号的相位.
  • 在任意的麦克风阵列上实现本地化方法,以实现高效的SNN处理.

主要成果:

  • 在声音源定位方面实现了高精度,与传统的超分辨率光束成形方法相美.
  • 在低功耗的SNN推断硬件上部署时,显著降低了功耗.
  • 验证了与SNNs共同设计信号处理的有效性,以提高功率效率.

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相关实验视频

Last Updated: May 28, 2025

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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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结论:

  • 提出的基于希尔伯特变换的光束成形方法为音源定位提供了一个计算效率高,准确的解决方案.
  • 这种方法非常适合超低功耗SNNs,使边缘设备中的高级音频处理成为可能.
  • 该方法还显示了提高传统数字信号处理效率的潜力.