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相关概念视频

Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

203
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...
203
Perception of Sound Waves01:01

Perception of Sound Waves

4.4K
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...
4.4K
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

195
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
195
Auditory Pathway01:15

Auditory Pathway

5.4K
Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
When viewed cross-sectionally, the cochlea reveals the scala vestibuli and scala tympani flanking...
5.4K
Sound Intensity00:58

Sound Intensity

4.0K
The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
4.0K
State Space Representation01:27

State Space Representation

200
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
200

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相关实验视频

Updated: Jun 19, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

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

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探索自我监督的对比学习空间声音事件表示.

Xilin Jiang1, Cong Han1, Yinghao Aaron Li1

  • 1Department of Electrical Engineering, Columbia University, USA.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|July 25, 2024
PubMed
概括

本研究引入了对比学习的多道框架 (MC-SimCLR),以改善空间音频理解. 该方法通过从未标记的数据中学习联合的光谱和空间表示来增强声音事件的分类和定位.

科学领域:

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

背景情况:

  • 空间音频分析需要理解声音事件及其位置.
  • 无监督学习方法对于利用大量未标记的音频数据至关重要.
  • 现有的方法可能无法有效地捕获音频的联合光谱和空间特征.

研究的目的:

  • 开发一个用于对比学习的多通道框架 (MC-SimCLR),以编码空间音频中的"什么"和"在哪里".
  • 从未标记的空间音频数据中学习联合的光谱和空间表示.
  • 为了增强下游任务,如事件分类和声音定位.

主要方法:

  • 提出了一个多层次的数据增强管道,增强波形,Mel光谱和通用交叉相关性 (GCC) 特性.
  • 引入了通道智能增强,包括麦克风顺序交换和Mel/GCC通道掩盖.
  • 利用对比式学习,从未标记的空间音频中学习联合的光谱和空间表示.

主要成果:

  • 在事件分类准确性方面,学习表征上的线性层显著超过监督模型.
  • 该框架在声音本地化方面实现了卓越的性能,减少了本地化错误.
  • 分析证实了个别增强方法和微调策略的有效性.
关键词:
相反的学习学习.自主监督学习学习声音事件定位和检测的声音事件定位和检测空间音频 空间音频

更多相关视频

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

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A Method to Study Adaptation to Left-Right Reversed Audition
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A Method to Study Adaptation to Left-Right Reversed Audition

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相关实验视频

Last Updated: Jun 19, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

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

Published on: August 9, 2024

1.5K
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
A Method to Study Adaptation to Left-Right Reversed Audition
07:14

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

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结论:

  • MC-SimCLR有效地学习空间音频的联合光谱和空间表示.
  • 拟议的增强策略对于框架的成功至关重要.
  • 这种无监督的方法为空间音频任务提供了监督方法的强大替代方案.