ACELP语音编码算法改进与仿真

代数码激励线性预测算法（ACELP）是目前诸多低速率语音编码标准的算法核心，包括3G语音标准VSELP、AMR、AMR—NB、AMR—WB．该算法基于码激励线性预测模型，通过对码本的有效搜索，确定基音延迟，算法时间复杂度为O（n^3）．本文在ACELP算法基础上，对ACELP中自适应码本搜索过程进行改进，提出E—ACELP算法．通过AMR标准中8种速率情况的仿真，E—ACELP算法码本搜索时间减少、时间复杂度下降．基于E—ACELP算法，语音编码标准性能和效率得到提高．

Improvement and Simulation for the ACELP Speech Encoding Algorithm

The algebra code - excited linear prediction （ACELP） is the core algorithm of a lot of low bit rate speech coding standards, including the 3G speech standard VSELP, AMR, AMR - NB, AMR - WB. The algorithm is based on code excited linear prediction model. The pitch delay is determined by the effective codebook search. The algorithm＇ s time complexity is O （n^3）. Based on the ACELP,this paper proposes an improved algorithm called E - ACELP. E - ACELP reduces the time of codebook search and time complexity through the simulation of eight ratios for AMR. The performance and efficiency of the standards based on ACELP are improved.