Speech enhancement based on kalman filtering, integrating with speech generation model, can be applied in non-stationary noise environment.
基于卡尔曼滤波的语音增强算法结合了语音的生成模型,并且适合于非平稳噪声干扰下的语音增强。
参考来源 - 基于卡尔曼滤波的语音增强算法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
在辨识实际系统时，非平稳噪声扰动是较多见的。
The systems disturbed by nonstationary noise are often encountered in the practical identification.
该算法无需先验知识和参考信道，且对平稳或非平稳噪声均适用。
This algorithm doesn't need priori knowledge or reference channel, and it can be used in the stationary or nonstationary noise environment.
实验表明：非平稳噪声在时域中是可以识别的，与传统的噪声频域检测方法相比，分辨率有明显的改善。
Simulation results show that non-stationary noise can be identified in time domain and resolution improvement is obvious compared with that of conventional noise detection in frequency domain.
应用推荐