A Speech Signal Dictionary Construction Method Based on New BDS Model

ZHANG Hong-le; LI Feng-lian; ZHANG Xue-ying

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A Speech Signal Dictionary Construction Method Based on New BDS Model

更新时间：2026-09-04

- A Speech Signal Dictionary Construction Method Based on New BDS Model
- Vol. 34, Issue 1, Pages: 30-34(2017)
- 作者机构：
  
  太原理工大学信息工程学院,山西,太原,030024
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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张宏乐, 李凤莲, 张雪英. 一种基于新型BDS模型的语音信号字典构造方法[J]. 微电子学与计算机, 2017, 34(1): 30-34.

ZHANG Hong-le, LI Feng-lian, ZHANG Xue-ying. A Speech Signal Dictionary Construction Method Based on New BDS Model[J]. 2017, 34(1): 30-34.
张宏乐, 李凤莲, 张雪英. 一种基于新型BDS模型的语音信号字典构造方法[J]. 微电子学与计算机, 2017, 34(1): 30-34. DOI：

ZHANG Hong-le, LI Feng-lian, ZHANG Xue-ying. A Speech Signal Dictionary Construction Method Based on New BDS Model[J]. 2017, 34(1): 30-34. DOI：

摘要

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.

Abstract

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.

关键词

Keywords

references

Xu Yangfei, Bao Guangzhao, Xu Xu, et al. Single-channel speech separation using sequential discriminative dictionary learning[J]. Signal Processing, 2015, 106(C): 134-140. DOI: 134

Aharon M, Elad M, Bruckstein A M. The k-svd: an algorithm for designing of overcomplete dictionaries for sparse representation[J]. IEEE Trans. Signal Process, 2006, 54 (11): 4311-4322. DOI: 4311

Yan Zhou, Heming Zhao, Xinyu Pan, et al. Lie detection from speech analysis based on K-SVD deep belief network model[J]. Springer International Publishing Switzerland, 2015, 9225(1): 189-196. DOI: 189

Zhou Yan, Zhao Heming, Shang Li, et al. Immune K-SVD algorithm for dictionary learning in speech denoising[J]. Neurocomputing, 2014, 137(5): 223-233. DOI: 223

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黄晓生, 黄萍, 曹义亲, 等. 一种改进的基于K-SVD字典学习的运动目标检测算法[J]. 微电子学与计算机, 2014, 31(3): 5-9. DOI: 5

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