Blind source separation (BSS) based on spatial time frequency distribution can separate signals with different time frequency distributions.
基于空间时频分布的盲源分离算法可以用来分离具有不同时频分布的信号。
The blind source separation fault diagnosis method based on Local wave time-frequency images is developed.
提出了一种基于局域波时频图像的盲源分离故障诊断方法。
Investigation characteristic of real world audio, combine stationary for short time-scale and non-stationary for longer time-scales, proposed a time frequency domain blind source separation algorithm.
研究了实际环境语音信号的特性，结合语音信号的短时平稳性和长时非平稳性，给出了一种时频域盲分离算法。
To obtain clearer desired signal under multi-speaker environment, the time-frequency masking effect is used in post-processing of speech enhancement using blind source separation.
本文将时频掩蔽效应引入盲源分离语音增强系统中，给出一种基于时频掩蔽效应和盲源分离的语音增强方法，并将其与期望最大化方法进行了性能比较。
Next, minimum mean square error (MMSE) filters are applied to the received signal to recover single user signal wave thus implementing user separation and frequency synchronization at the same time.
然后，对接收到的信号应用最小均方误差滤波恢复单用户信号波形，从而同时实现了多用户信号分离及频率同步；
Next, minimum mean square error (MMSE) filters are applied to the received signal to recover single user signal wave thus implementing user separation and frequency synchronization at the same time.
然后，对接收到的信号应用最小均方误差滤波恢复单用户信号波形，从而同时实现了多用户信号分离及频率同步；
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