一种基于新型BDS模型的语音信号字典构造方法

A Speech Signal Dictionary Construction Method Based on New BDS Model

用字典训练的方法稀疏表示语音信号成为语音信号处理领域的热点课题.针对字典初始规模的选择问题,提出了一种新的基于新型BDS模型的字典训练方法,该方法根据最佳字典规模与稀疏比的关系为字典规模建立模型,可以自适应的为语音信号选择恰当的初始字典规模,克服了K-SVD方法依靠经验设置字典规模的缺陷.将加入BDS模型的训练字典的方法应用于来自太原理工大学数字音频与视频实验室语音库的语音,进行仿真实验并对实验结果进行了分析.实验结果表明:基于BDS模型的语音信号字典构造方法实现了自适应选择最佳字典规模目的,可在保证重构语音质量的同时,进一步提高字典训练的效率.

Recently,using a dictionary learning method to sparse represent speech signal becomes a popular research subject.For selecting the initial dictionary size problem,it is proposed a new dictionary training method that based on new BDS model.This method established model for dictionary size according to the relationship between dictionary size and sparse ratio.This model can self-adaptively choose properly initial dictionary for speech signal.More over,it overcomes the defects of selecting initial dictionary size in K-SVD method on experience.Appling this training dictionary method added the BDS model to speech library of lab of audio and video of Taiyuan University of Technology by simulating experiments.The results are analyzed.Simulation results show that the based on BDS model speech signal dictionary construction method not only realized that choose best dictionary size adaptively,but also improved the efficiency and stability of the dictionary training.