Description: 语音信号去噪，在MATLAB开发环境下，对含噪声的语音信号进行小波变换，根据阈值去噪的原理去噪，然后逆变换得到增强的语音信号-Speech Signal Denoising in the MATLAB development environment, including noise on the voice signal wavelet transform, in accordance with the principle of threshold denoising Denoising, and then inverse transform enhanced speech signal Platform: |
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Author:王鹏 |
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Description: 说明小波消噪的理论，阈值估算和提取，给出在MATLAB下阈值估算和消噪的函数，并在语音信号处理中增强语音。-Explain the theory of wavelet denoising, threshold estimation and extraction, are given in the MATLAB under the threshold estimation and de-noising function, and speech signal processing to enhance voice. Platform: |
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Author:宋知用 |
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Description: ：由于许多传统的去噪方法在强背景噪声情况下提取声音信号的能力变弱甚至失效， 提出
应用独立成分分析( I C A) 方法对声音信号进行特征提取， 并证明了这种 I C A 变换能增强语音和音
乐信号的超高斯性． 在此基础上， 应用 I C A基函数作为滤波器， 通过阈值化的去噪方法对含有强高
斯背景噪声的声音信号进行去噪仿真实验． 结果表明， 本方法明显优于传统的均值滤波和小波去噪
方法， 为强背景噪声下弱信号的检测提供 了新的途径．-: As many of the traditional de-noising method in case of strong background noise, the ability to extract the voice signal even weaker failure, the application of independent component analysis (ICA) method of voice signal feature extraction, and prove that this transformation can be enhanced voice ICA and music of super-Gaussian signals. On this basis, the application of ICA basis function as a filter, through the threshold of the denoising method of Gaussian background noise contains strong voice signal denoising simulation. The results show that this method is obviously superior to the traditional mean filtering and wavelet denoising methods for the strong background noise under the weak signal detection provides a new way. Platform: |
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Author:金振东 |
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Description: 为提高语音端点检测(VAD)在较低信噪比(10 dB)下的准确率,提出一种基于短时分形维数的改进算法。结合语音信号的特点,对2种常用的语音信号分形维数计算方法进行了比较和选择,同时采用动态跟随门限值实现语音端点的自适应检测。试验结果表明:对于信噪比6~10 dB的带噪语音,此方法可以实现整段语音的检测,而且具有一定的噪声鲁棒性,系统运行期间能够自适应调整门限值以适应环境噪声的变化,提高了VAD算法的准确率。这个是源码matlab。-In order to improve voice activity detection (VAD) in low SNR (10 dB) accuracy under proposed based on short-time fractal dimension of the improved algorithm. Combined with the characteristics of the speech signal, to 2 commonly used fractal dimension of speech signals are compared and calculated choice to follow the same dynamic endpoint threshold adaptive detection of voice. The results showed that: 6 ~ 10 dB for the signal to noise ratio of noisy speech, this method can detect the entire speech, but has some noise robustness, the system can be adaptively adjusted during operation to adapt to environmental noise threshold of changes to improve the accuracy of VAD algorithms. This is the source matlab. Platform: |
Size: 79872 |
Author:liuhongfu |
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Search - voice signal threshold in matlab