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Perception of Sound Waves01:01

Perception of Sound Waves

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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
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一个移动的Deep Sparse Wavelet自动编码器用于阿拉伯语声学单元建模和识别.

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  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

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一个新的Deep Sparse Wavelet Network (DSWN) 通过使用深度学习和稀疏编码,有效地模拟移动设备上的声学单元. 这种方法对语音单元分类有希望,减少了计算负载.

关键词:
声学单位 声学单位深度学习是一种深度学习.深度稀疏波纹网络深度稀疏波纹网络梅尔频率的塞普斯特拉尔系数移动架构的移动架构感知线性预测的感知线性预测.堆叠的波形自动编码器.波形网络 (wavelet) 是一种波形网络.

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科学领域:

  • 语音处理 语音处理
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 声学单元建模对于语音识别至关重要.
  • 移动设备需要计算效率高的算法.
  • 集成深度学习,稀疏编码和波纹网络为改进声学建模提供了潜力.

研究的目的:

  • 为声学单元建模引入一种新的深波浪网 (DSWN).
  • 设计一个适合移动架构的DSWN,以减少计算开销.
  • 使用Mel频率 cepstral系数 (MFCC) 和感知线性预测 (PLP) 特性来分类和区分声学单位.

主要方法:

  • 通过集成堆叠的波形自动编码器开发了一个深度稀疏波形网络 (DSWN).
  • 利用Mel频率 cepstral 系数 (MFCC) 和感知线性预测 (PLP) 特性用于语音单元编码.
  • 设计了以最小连接的深度网络,以减少移动部署的计算复杂性.

主要成果:

  • 证明了DSWN系统在阿拉伯单词的细分语料库上的有效性.
  • 拟议的DSWN实现了声学单元的有效分类和区分.
  • 该方法成功地减少了移动应用程序的计算开销.

结论:

  • 深度稀疏波浪网 (DSWN) 为移动设备上的声学单元建模提供了一种有效和计算效率高的方法.
  • 需要进一步的研究来评估DSWN对不同语境和语音变异的概括性.
  • 未来的工作将调查口音和其他语音变异对模型性能的影响.