| 摘要: |
| 语音信号的清浊音辨别及基音周期的提取是语音信号处理的重要组成部分。提出了一种基于压缩感知（CS）观测序列的语音基音周期提取方法,大大减少了基音周期提取的计算量。首先分析不同观测矩阵下观测序列的特征，选择了行阶梯矩阵作为所提方法的观测矩阵；然后对此观测矩阵下的观测序列做小波分解，对小波分解的低频系数采用自相关方法提取基音周期。仿真与分析表明，和传统方法比较，所提方法与传统方法基音检测准确度相当，且在语音含有高斯白噪声的条件下运行良好,但计算量仅约为传统方法的1/16。 |
| 关键词: 语音信号处理 基音周期 压缩感知 非重构 自相关函数 小波变换 |
| DOI： |
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| 基金项目:国家自然科学基金资助项目(61271335) |
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| Pitch detection based on compressed sensing sequence |
| CAO Yang |
| () |
| Abstract: |
| Voiced/unvoiced decision and pitch detection technique is an important part of speech signal processing.Based on the observation sequence of compressed sensing(CS)，a method to detect the pitch is proposed in this paper,and the caculation amount is greatly reduced .Firstly, the characteristics of observation sequences under different measurement matrixs are analyzed,and row echelon matrix is selected as the measurement matrix.Then wavelet decomposition with the observation sequence is performed and low frequency coefficients of the result is used for pitch extraction through autocorrelation method.Simulation and analysis show that compared with traditional autocorrelation method, the pitch detection accuracy of this method is almost the same as that of the traditional method, and it runs well when the speech is interfused with Gaussian white noise, but the calculation amount is just about 1/16 of that of the traditional method. |
| Key words: speech signal processing pitch period compressed sensing(CS) non-reconstruction autocorrelation function wavelet decomposition |
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