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

Reducing Line Loss01:18

Reducing Line Loss

196
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
196
Downsampling01:20

Downsampling

260
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
260
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Classification of Signals01:30

Classification of Signals

908
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
908
Sample Handling01:02

Sample Handling

151
Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
Samples should be transported carefully from collection points to the laboratory. They should be properly sealed and clearly labeled to prevent cross-contamination. To preserve the sample integrity, optimal temperature conditions during transport are essential. This could involve using...
151
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

357
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
357

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 author

The relationship between anxiety and patent foramen ovale: a preliminary study.

BMC psychology·2026
Same author

Impedance Analysis of Porous-Material-Functionalized RF Sensors toward Intelligent E-Nose and E-Tongue for Multidisciplinary Monitoring.

ACS applied materials & interfaces·2026
Same author

Effects of L-Citrulline Supplementation on Rumen Microbiota and Reproductive Performance of Ewes.

Life (Basel, Switzerland)·2026
Same author

A long non-coding RNA-messenger RNA regulatory network associated with residual feed intake in the olfactory tissue of meat ducks.

Poultry science·2026
Same author

SARLite: enhanced lightweight YOLO framework for SAR object detection.

Scientific reports·2026
Same author

Analysis of complete blood counts and derived inflammatory indices with non-suicidal self-injury and suicide attempt in adolescents with major depressive disorder.

Frontiers in psychiatry·2026

相关实验视频

Updated: Sep 16, 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

1.6K

通过动态样本丢失结合在语音分离中的层级优化来改进标签分配学习.

Chenyang Gao1, Yue Gu2, Ivan Marsic1

  • 1Rutgers University.

Interspeech
|July 8, 2025
PubMed
概括

动态样本丢失和层级优化通过减少标签分配切换和层分离来改善监督语音分离. 这种新的方法增强了模型学习,以获得更好的语音分离性能.

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:

  • 变换不变训练 (PIT) 是监督语音分离的标准,解决了标签模两可的问题.
  • 在PIT中过度的标签分配切换阻碍了有效的模型学习.
  • 层分离问题也会影响语音分离性能.

研究的目的:

  • 引入一种新的培训策略,以减轻语音分离中的标签分配切换.
  • 通过解决层脱来提高语音分离性能.
  • 在监督语音分离模型中增强最佳标签分配的学习.

主要方法:

  • 动态样本丢失 (DSD):不包括基于先前性能对标签分配产生负面影响的样本.
  • 层智能优化 (LO):解决层脱问题,以提高性能.
  • 实施了联合DSD和LO策略,用于监督语音分离.

主要成果:

  • 结合的DSD和LO方法显著超过了基线方法.
  • 拟议的战略有效地解决了过度的标签分配切换问题.
  • 成功解决了层分离问题,从而提高了性能.

结论:

关键词:
语音分离 语音分离 语音分离动态样本脱落情况 动态样本脱落情况层层的优化优化方法调配不变训练的培训方法

更多相关视频

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.6K

相关实验视频

Last Updated: Sep 16, 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

1.6K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.6K
  • DSD和LO策略为监督语音分离的挑战提供了有效的解决方案.
  • 这种方法很容易实现,不需要额外的数据或培训步骤,并且在不同的语音分离任务中具有多功能.