摘要
针对现有基于字典学习的增强算法需要先验信息、不易实时处理的问题,提出一种便于实时处理的无监督的单通道语音增强算法。首先,该算法将无监督条件下背景噪声的建模问题转化为带噪语音幅度谱的稀疏低秩噪声分解;然后,采用增量非负子空间方法对背景噪声进行在线字典学习,获得能够体现背景噪声时变特性的自适应噪声字典;最后,利用所得的噪声字典,采用易于实时处理的逐帧迭代方式,对带噪语音进行处理。实验结果表明:相较于多带谱减法和基于低秩稀疏矩阵分解的增强算法,所提算法在噪声抑制方面的性能尤为显著,在多项性能评价指标上,均表现出更好的结果。
An unsupervised speech enhancement algorithm suitable for real-time processing in one channel record is proposed, aiming at resolving the prior-information-reliance and real-time processing difficulty in existing enhancement algorithms based on dictionary learning. With the magnitude of noisy speech, it recasts unsupervised background noise modeling problem into sparse, low-rank and noise decomposition. Subsequently, an adaptive noise dictionary which reflects the dynamic noise background is learned in an online fashion by employing incremental nonnegative subspace learning. Finally, frame-by-frame enhancement is conducted with the learnt dictionary, which makes the real-time processing much more convenient. Extensive experiments demonstrate that the presented algorithm outperforms state-of- the-art method such as multi-band spectral subtraction and method based on low-rank and sparse matrix decomposition, especially in terms of noise reduction.
出处
《声学学报》
EI
CSCD
北大核心
2015年第4期607-614,共8页
Acta Acustica
基金
国家自然科学基金(61072042
61402519
61471394)
江苏省自然科学基金(BK2012510
BK20140071
BK20140074)资助
