Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes.
由于分解是基于信号时域局部特征的，因此它特别适合用来分析非线性非平稳过程。
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。
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。
应用推荐