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本文针对最大长度序列相关(Maximal Length sequence Correlation,MLc)建模技术在窄带主动噪声控制系统次声学路径建模的应用中,系统性能易受窄带信号影响这一不足,提出了一种改进的MLC(MMLC)次声学路径建模技术。具体地说就是采用一个自适应预测滤波器来预测和消除MLC技术中的窄带干扰,并用一个补偿滤波器来修正由预测误差滤波器引起的训练信号成分失真。计算机仿真表明,MMLC算法能有效克服窄带主动噪声控制系统次声学路径建模的窄带信号影响,具有较高的建模精度。 相似文献
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后置滤波技术作为语音增强的一种方法在语音编码中得到广泛的应用.8kbps QCELP是一种变速率语音编码算法,其自适应后置滤波器由短时后置滤波器、频谱倾斜补偿滤波器和自动增益控制(AGC)三部分组成.这种自适应后置滤波器能够显著改善合成语音的主观质量,而且语音失真小,计算复杂度低.本文详细介绍了自适应后置滤波器中各个组成部分,并与其它变速率语音编码中的后置滤波器进行了比较. 相似文献
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提出一种基于GSC的语音增强算法,该算法应用了DFT调制子带滤波器组将语音信号分解到子带进行自适应滤波,从而获得更好的增强效果以及更低的运量复杂度.同时,将范数约束自适应滤波(NCAF)算法应用于自适应噪声对消器(ANC)以降低语音的失真度.为了进一步去除增强后语音中的残留噪声,算法使用改进的Wiener后置滤波器.仿真结果表明,相对于基于全带GSC的麦克风阵列语音增强算法以及传统Wiener后置滤波算法,采用本文所用算法具有更高的输出分段信噪比. 相似文献
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噪声对消在信号处理系统中的应用 总被引:3,自引:1,他引:2
论述基于LMS算法的自适应滤波器噪声对消的工作原理,以及基于AR模型的信号分析方法。在这两种方法相结合的情况下,能有效去除信号的噪声。对含有瞬态干扰的微弱信号,用AR模型法估计出噪声的系数并预测噪声波形,通过自适应滤波器噪声对消原理进行滤波,最后在Matlab环境下进行仿真试验,结果表明该方法具有较好的去噪效果。 相似文献
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语音通信中的自适应噪声对消系统设计 总被引:1,自引:0,他引:1
现实中的语音通信可能发生在嘈杂的背景噪声中,采用噪声对消技术可以改善噪声环境中的语音通信效果。基于自适应滤波原理,实现了一种适用于语音通信的自适应噪声对消系统。仿真结合语音信号的特点,比较了不同步长因子对系统性能的影响,并给出了优化设计的系统参数。软件编程采用DSP专门的自适应滤波指令,满足了系统对软件实时性的要求。 相似文献
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基于sigmoid函数的Volterra自适应有源噪声对消器 总被引:6,自引:0,他引:6
该文介绍了一种新颖的非线性自适应有源噪声对消器基于sigmoid函数的Volterra自适应有源噪声对消器,并采用输入信号和瞬时误差归一化的LMS自适应算法调整其系数。这种基于sigmoid函数的Volterra自适应有源噪声对消器具有参数少和便于实现的模快化结构等优点。仿真结果表明:这种基于sigmoid函数的Volterra自适应有源噪声对消系统具有良好的抗噪声性能。 相似文献
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基于自适应滤波的噪声抵消法 总被引:4,自引:1,他引:4
语音降噪就是从带噪语音信号中提取尽可能纯净的原始语音。文中介绍了一种基于自适应滤波的噪声抵消法,采用归一化最小均方误差算法,采集实际噪声环境下各种不同信噪比的带噪语音样本进行降噪处理,实验结果表明,处理后信号的信噪比得到了较大程度的提高,大大改善了听音效果,具有很高的可懂度,且语音自然度好,没有失真;并与谱减法进行了比较,自适应噪声抵消法的降噪幅度比谱减法有一定提高,在听音效果上,用自适应噪声抵消法处理后的语音在清晰度、自然度方面优于谱减法。 相似文献
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本文为了在语音信号处理中能消除含噪语音信号中的背景噪音,采用自适应信号处理的理论和技术来达到提高语音信号质量的目的。通过介绍自适应滤波器原理,在对自适应滤波器相关理论研究的基础上,研究了LMS自适应滤波算法,并对LMS自适应算法进行了分析。同时为了使输入的参考信号与噪声相关,加入分离周期信号与带有窄带干扰抑制的宽带信号。通过分析仿真结果表明基于LMS算法的自适应噪声抵消技术可以有效地抵消正弦干扰信号,同时加入宽带信号中的周期性噪声,在没有另外的与噪声相关的参考信号的情况下,可以使用自适应噪声抵消系统来消除这种同期性干扰噪声。 相似文献
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Signal separation by symmetric adaptive decorrelation: stability,convergence, and uniqueness 总被引:4,自引:0,他引:4
The performance of signal enhancement systems based on adaptive filtering is highly dependent on the quality of the noise reference. In the LMS algorithm, signal leakage into the noise reference leads to signal distortion and poor noise cancellation. The origin of the problem lies in the fact that LMS decorrelates the signal estimate with the noise reference, which, in the case of signal leakage, makes little sense. An algorithm is proposed that decorrelates the signal estimate with a “signal-free” noise estimate, obtained by adding a symmetric filter to the classical structure. The symmetric adaptive decorrelation (SAD) algorithm no longer makes a distinction between signal and noise and is therefore a signal separator rather than a noise canceler. Stability and convergence are of the utmost importance in adaptive algorithms and hence are carefully studied. Apart from limitations on the adaptation constants, stability around the desired solution can only be guaranteed for a subclass of signal mixtures. Furthermore, the decorrelation criterion does not yield a unique solution, and expressions for the “phantom” solutions are derived. Simulations with short FIR filters confirm the predicted behavior 相似文献
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FIR自适应滤波的语音增强算法 总被引:2,自引:1,他引:1
提出一种基于线性预测FIR自适应滤波的语音增强算法,该算法可实时过滤被噪声污染的语音信号,提高信噪比,从而提高语音识别系统的识别率。仿真结果证明该算法具有较好的降噪效果。 相似文献
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研究了只能获得带噪信号的情况下的语音增强问题。将语音信号看作由高斯噪声激励的自回归(AR)过程,观测噪声为加性高斯白噪声,把信号转化为状态空间模型。首先用隐马尔可夫模型(HMM)估计AR参数和噪声的方差作为卡尔曼滤波器初值,估计信号作为参数估计的中间值给出,然后将估计信号通过一个感知滤波器平滑以消除残余噪声。仿真结果表明该算法有良好的性能。 相似文献
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Applications of adaptive filtering to ECG analysis: noisecancellation and arrhythmia detection 总被引:7,自引:0,他引:7
Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm. 相似文献
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Speech enhancement algorithms play an important role in speech signal processing. Over the past several decades, many algorithms have been studied for speech enhancement. A speech enhancement algorithm uses a noise removal method and a statistical model filter to analyze the speech signal in the frequency domain. Spectral subtraction and Wiener filters have been used as representative algorithms. These algorithms have excellent speech enhancement performance, but suffer from deterioration in performance due to specific noise or low signal-to-noise ratio (SNR) environments. In addition, according to estimations of erroneous noise, a noise existing in a voice signal is maintained so that a spectrum corresponding to a voice signal is distorted, or a frame corresponding to a voice signal cannot be retrieved, and voice recognition performance deteriorates. The problem of deterioration in speech recognition performance arises from the difference between speech recognition and training model. We use silence-feature normalization model as a methodology to improve the recognition rate resulting from the difference in the noisy environments. Conventional silence-feature normalization has a problem in that the silent part of the energy increases, which affects recognition performance due to unclear boundaries categorizing the voice. In this study, we use the cepstrum feature of the noise signals in the silence-feature normalization model to improve the performance of silence-feature normalization in a signal with a low SNR by setting a reference value for voiced and unvoiced classification. As a result of recognition rate confirmation, the recognition rates improve in performance, compared with other methods. 相似文献
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