结合幅度谱和功率谱字典的语音增强方法

从双路字典学习、噪声功率谱估计、语音幅度谱重构角度提出了一种改进的谱特征稀疏表示语音增强方法。在字典学习阶段,融合功率谱与幅度谱特征,采用区分性字典降低语音字典和噪声字典的相干性;在语音增强阶段,提出一种噪声功率谱估计方法对非平稳噪声进行跟踪估计;考虑到幅度谱和功率谱特征对不同噪声的适应程度不同,设计了语音重构权值表。对分别由幅度谱和功率谱恢复而来的两路信号进行自适应加权重构,结合相位补偿函数得到增强后的语音信号。实验结果表明,该方法在平稳、非平稳噪声环境下相比于单一谱特征的语音增强方法平均提高31.6%,改善了语音增强方法的性能。 

Speech enhancement method combining amplitude spectrum and power spectrum dictionary

An improved speech enhancement method based on sparse representation of spectral features is proposed from the perspective of dual dictionary learning,noise power spectrum estimation and speech amplitude spectrum reconstruction.In the dictionary learning stage,the power spectrum and amplitude spectrum features are combined,and the discriminative dictionary learning method is used to reduce the coherence of the speech dictionary and the noise dictionary.In the speech enhancement stage,a noise power spectrum estimation method is proposed to track and estimate the non-stationary noise.Considering the inconsistency between the amplitude spectrum and the power spectrum characteristics for different noises,a speech reconstruction weight table is designed.The two way signals recovered from the amplitude spectrum and the power spectrum are adaptively weighted,and the enhanced speech signal is obtained by combining the phase compensation function.As shown in experimental results that,compared with the single spectral feature speech enhancement method,this method improves the average of 31.6% in stationary and non-stationary noise environments,improving the performance of the speech enhancement method.