为提高噪声模型的估计精度，改善系统率失真性能，文中提出了一种基于残差子带分组聚类的自适应噪声模型估计方法． 首先根据频率高低对残差子带进行分组，然后由组内子带残差样本生成特征矢量，进而利用改进的模糊 c-均值聚类算法对当前解码子带进行聚类，最后计算出每类残差系数的噪声参数． 实验结果表明，相比于相邻子带聚类－方差估计算法，文中所提算法能够更加准确地匹配残差分布特征，率失真性能平均提升0．60dB，且解码时间平均节省 40．59%．

In order to improve the estimation accuracy of noise model and the rate-distortion performance of the sys-tem，an adaptive noise model estimation method on the basis of residual sub-band grouping is proposed． In this method，firstly，residual sub-bands are grouped according to their frequencies． Secondly，feature vectors are generated from the residual coefficients of all sub-bands in the same group． Then，the coefficients in each sub-band are clustered into different classes by means of improved fuzzy c-means clustering． Finally，the noise para-meters of each class of residual coefficients are estimated successfully． Experimental results show that，in compari-son with the method on the basis of adjacent sub-band clustering and variance estimation，the proposed method matches the residual distribution characteristics more accurately，improves the average rate-distortion performance by 0. 60dB，and saves the decoding time by 40. 59%．