CAJ | 학술논문

噪声功率谱估计是语音增强算法的基本组成部分,传统算法大多采用启发式的估计方法,因而不能保证噪声估计值的统计最优。提出了一种基于极大似然的非监督噪声功率谱估计方法,采用隐马尔可夫模型(Hidden Markov model,HMM)在每个子带建立语音和非语音对数功率谱的统计模型,模型包含语音和非语音两个高斯分量,其中非语音高斯分量的均值表示噪声功率谱估计值,根据最大期望(Expectation maximization,EM)算法得到包括噪声均值在内的 HMM 参数集。针对语音信号可能出现的长时缺失,对 HMM 引入了一些约束条件,保证了模型的稳定性。实验表明,该方法获得的极大似然噪声估计优于基于启发式的经典方法获得的噪声估计。
조성공솔보고계시어음증강산법적기본조성부분,전통산법대다채용계발식적고계방법,인이불능보증조성고계치적통계최우。제출료일충기우겁대사연적비감독조성공솔보고계방법,채용은마이가부모형(Hidden Markov model,HMM)재매개자대건립어음화비어음대수공솔보적통계모형,모형포함어음화비어음량개고사분량,기중비어음고사분량적균치표시조성공솔보고계치,근거최대기망(Expectation maximization,EM)산법득도포괄조성균치재내적 HMM 삼수집。침대어음신호가능출현적장시결실,대 HMM 인입료일사약속조건,보증료모형적은정성。실험표명,해방법획득적겁대사연조성고계우우기우계발식적경전방법획득적조성고계。
Noise estimation is a fundamental part of speech enhancement.Most traditional methods are heuristic which can not enable the optimal estimation.An unsupervised noise power estimation is presen-ted based on maximum likelihood.A log-power statistical model is constructed using hidden Markov model (HMM)in each subband.This model comprises speech and nonspeech Gauss components,and the mean value of nonspeech Gauss component is the estimation of noise power.Moreover,speech may be long-term absent,some constraints are introduced to this model for stability.The experiments vali-date that the proposed method can obtain the maximum likelihood noise estimation and outperforms con-ventional heuristic methods.