CAJ | 학술논문

为了进一步提高增强语音的质量，基于传统的贝叶斯非负矩阵分解语音增强算法，考虑语音帧内原子间的相关性，提出了一种新的改进贝叶斯非负矩阵分解语音增强算法。该算法可分为训练和增强2个阶段：训练阶段利用该算法分别对纯净语音和噪声进行训练，得到纯净语音和噪声字典；增强阶段利用训练得到的纯净语音和噪声字典组成的联合字典结合，计算带噪语音时变增益，并利用最小均方误差估计得到增强语音频谱，进而重构增强语音。实验结果表明，该算法的对数频谱距离值和主观语音质量评估打分均优于非负矩阵分解(NMF)和贝叶斯非负矩阵分解(BNMF)等传统的语音增强算法，特别是在低信噪比条件下，该算法增强的效果更佳。
위료진일보제고증강어음적질량，기우전통적패협사비부구진분해어음증강산법，고필어음정내원자간적상관성，제출료일충신적개진패협사비부구진분해어음증강산법。해산법가분위훈련화증강2개계단：훈련계단이용해산법분별대순정어음화조성진행훈련，득도순정어음화조성자전；증강계단이용훈련득도적순정어음화조성자전조성적연합자전결합，계산대조어음시변증익，병이용최소균방오차고계득도증강어음빈보，진이중구증강어음。실험결과표명，해산법적대수빈보거리치화주관어음질량평고타분균우우비부구진분해(NMF)화패협사비부구진분해(BNMF)등전통적어음증강산법，특별시재저신조비조건하，해산법증강적효과경가。
To improve the quality of the enhanced speech,a novel improved speech enhancement algorithm via traditional Bayesian nonnegative matrix factorization (BNMF)was proposed.The correlations of atoms in speech frame were considered in the improved BNMF (IBNMF).The proposed IBNMF consists of a training stage and an enhancing stage.During the training stage,the training sets of speech and noise were analyzed by IBNMF algorithm,and the dictionaries of speech and noise constructed.In the enhancing stage,combining the dictionaries of speech and noise,the coding matrix of speech was evaluated from the spectrum of noisy speech.Then,the spectrum of enhanced speech was derived by minimum mean square error (MMSE)estimator and the enhanced speech reconstructed.Experimental results show that the scores of log spectral distance(LSD)and perceptual evaluation of speech quality(PESQ)via IBNMF are better than the traditional speech enhancement methods,such as NMF and BNMF,especially in low SNR conditions.