基于中国餐馆过程的语音增强

针对有色噪声中的语音增强问题，通过引入中国餐馆过程混合模型（Chinese Restaurant Process Mixture Model，CRPMM），其潜变量满足中国餐馆过程，能够较方便地获得马尔科夫链式样本的展开.建立了参变量与潜变量基于块采样的后验更新形式，结合卡尔曼滤波技术，能够在分布空间上更精确地逼近噪声的后验分布.仿真算例及实际语音信号增强算例表明，较之传统的参数化卡尔曼滤波算法及变分贝叶斯滤波算法，基于数据驱动的无穷维的块采样技术能够更好地适应新模态，并取得较好的语音增强效果.

Speech Enhancement Based on Chinese Restaurant Process

Aiming to the problem of speech enhancement from colored noise, the Chinese restaurant process mixture model (referred to as CRPMM) was introduced to modeling the distribution of noise. Markov sampling chains can be conveniently extended, where the latent variables are distributed according to a Chinese restaurant process. The updating form of joint probability of parametric variables and the latent variables were built related to block sampling. This method can approximate the posterior distribution of noise on space exactly, incorporating Kalman filtering technique. The simulation results and the experimental result of speech enhancement show that the infinite block sampling technique data-driving can adapt new modes, and a higher efficiency can be achieved for speech enhancement.