Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same journal

Electroacoustic single-cell spectroscopy for morphological differentiation of red blood cells: An in silico feasibility study.

JASA express letters·2026
Same journal

Evaluating peripheral neural processing through spectral analysis of auditory brainstem responses.

JASA express letters·2026
Same journal

Coarticulation vs Phonologization: Evidence from Southern Italo-Romance.

JASA express letters·2026
Same journal

A multi-task neural network for source localization in shallow-water environment with depth classification.

JASA express letters·2026
Same journal

Frequency modulation detection in Mandarin Chinese-speaking amusics across modulation rate, carrier frequency, and stimulus duration.

JASA express letters·2026
Same journal

Spectral weighting of interaural time differences near the low-frequency dominant region in young and middle-aged adults.

JASA express letters·2026

相关实验视频

Updated: May 24, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

空间分组作为一种改善个性化头部相关转移函数预测的方法.

Keng-Wei Chang1, Yih-Liang Shen1, Tai-Shih Chi1

  • 1Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Taiwan 300093alex0976296586@gmail.com, yihliang.ee06@nycu.edu.tw, tschi@nycu.edu.tw.

JASA express letters
|March 3, 2025
PubMed
概括

我们提出了一种新的方法,通过空间分组HRTF数据来估计与头部相关的转移函数 (HRTF). 这种方法平衡了计算效率和性能,优于现有的模型.

科学领域:

  • 声学和音频工程 声学和音频工程
  • 机器学习和人工智能的人工智能

背景情况:

  • 与头部相关的传输函数 (HRTF) 对空间音频感知至关重要.
  • 准确的HRTF估计是计算密集的,特别是使用神经网络模型.
  • 现有的特定角度模型提供了高性能,但需要大量的计算资源.

研究的目的:

  • 开发一种计算效率高的HRTF估计方法.
  • 提高基于神经网络的HRTF预测模型的性能.
  • 在HRTF估计中平衡性能和计算成本.

主要方法:

  • 提出一种新的方法,涉及将HRTF数据空间分组成子空间.
  • 通过有针对性的数据分组,减少每个子空间内的差异.
  • 训练个人HRTF预测每个子空间的神经网络.

主要成果:

  • 与全球模型相比,拟议的方法显示出更高的性能.
  • 空间分组方法的表现优于传统的角度特定模型.
  • 不同的分组策略对ipsilateral和contralateral方面显示出有效性.

结论:

更多相关视频

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.6K
Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging
09:33

Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging

Published on: November 15, 2024

1.1K

相关实验视频

Last Updated: May 24, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.6K
Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging
09:33

Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging

Published on: November 15, 2024

1.1K
  • 空间分组HRTF数据为提高预测准确性提供了一个有效的策略.
  • 拟议的方法为HRTF估计提供了一个平衡的解决方案,优化性能和计算负载.
  • 这项技术推动了个性化空间音频和虚拟声学领域的发展.