共查询到17条相似文献,搜索用时 780 毫秒
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针对现有双通道语音活动检测(Voice Activity Detection, VAD)算法依赖于固定阈值难以在多种噪声环境下准确地检测语音和噪声,应用于手机消噪系统会造成语音失真或噪声消除不好等问题,该文提出一种基于神经网络的VAD算法,该算法以分频带能量差和归一化互通道相关为特征,采用神经网络对语音和噪声进行分类。在此基础上,将神经网络VAD与基于互通道信号功率比值的VAD相结合,提出一种新的适用于手机消噪系统的语音和噪声活动检测算法分别对语音和噪声进行检测,并以此进行噪声抑制处理,减少了消噪系统因VAD误判而造成的性能下降。实验结果表明,该处理方法在抑制背景噪声和减少语音失真等方面优于现有的消噪算法,对于方向性语音干扰也有很好的抑制效果。 相似文献
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讨论了一种基于传统谱相减算法的改进方法。利用语音的短时平稳性,通过先验幅度比来连续更新噪声谱的估计,从而代替复杂的VAD(话音活性检测)。计算机仿真结果表明,这种改进方法有效抑制了噪声干扰,语音得到了增强,在极大地提高信噪比的同时,将残留的音乐噪声和语音失真保持在人耳听觉容忍的范围以内,从而较好的保持了语音自然度。 相似文献
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在多媒体会议房间中,鼓掌、咳嗽等非高斯干扰噪声常会严重影响语音处理系统的性能.为了有效地抑制非高斯干扰噪声,本文提出了一种基于线性预测残差域高阶统计量的语音VAD检测方法.该方法利用语音信号线性预测残差的归一化峰度表征语音和非语音信号在谐波数量上的差异,构造判别准则进行VAD检测,并通过预估高斯背景噪声的能量,削弱了背景噪声对VAD算法性能的影响.仿真实验结果表明,该方法能够有效地区分高斯背景噪声下的语音和非高斯噪声. 相似文献
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本文提出了一种对于不同背景噪音具有鲁棒性的语音激活检测(VAD)算法.首先,该算法基于统计模型理论、线性预测原理以及自适应时变噪声参数估计方法,在时域和频域中共提取了四个特点不同的特征参数作为分类器的输入特征矢量,然后应用支持向量机(SVM)的方法,进行语音激活检测.最后,通过在不同噪音环境下的对比实验结果,验证了本文所提出的算法在中低信噪比情况下的检测性能要优于ITU G.729B中的VAD算法. 相似文献
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通过介绍语音增强的特点,详细分析了最小均方误差对数谱幅度估计(MMSE-LSA)算法,并提出了与MMSELSA算法相匹配的语音激活检测(VAD)算法。该方案计算简单、易于实现且语音增强效果好,能够动态地跟踪背景噪声的变化。最后通过仿真分析,比较了MMSE-LSA与其它几种语音增强算法的增强效果。 相似文献
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提出了一种提高目前GSM系统中Abis接口线路传输能力的方法-利用语音通信的VAD和DTX进行话疸的倍增复用,并对其原理、实现方法、传输性能和影响进行了较为深入的探讨。 相似文献
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结合语音激活检测(VAD)技术对短时对数谱估计最小均方误差(LSA-MMSE)语音增强算法进行了改进。通过实验表明,LSA-MMSE增强算法在消除背景噪声、增加语音清晰度和提高语音自然度等方面比谱减法更加有效。 相似文献
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一种基于支持向量机的含噪语音的清/浊/静音分类的新方法 总被引:10,自引:3,他引:7
本文将支持向量机(SVM)方法应用于语音信号的清/浊/静音检测中,提出并验证了一种在各种信噪比等级下将语音信号有效地分为清音、浊音和静音三类信号的新型分类算法.首先,在高信噪比情况下,本文采用了G.729B VAD中的四个差分参数作为SVM分类器的输入特征参数,进行了静音分类的对比实验,得到了优于G.729B VAD和BP神经网络传统算法的实验结果,说明引入这种机器学习方法做语音分类是可行的,并分析讨论了在核函数不同的情况下支持向量机在实验中所表现出的性能.其次,又讨论了在低信噪比条件下,如何通过对含噪语音建立统计模型,提取对噪音免疫的统计特征参数,并给出了一种对时变背景噪声自适应的估计方法.最后,通过在不同噪音环境下的对比实验结果,验证了本文所提出的算法在中低信噪比情况下的分类性能要优于其他传统算法. 相似文献
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Most of Voice activity detection (VAD) methods are based on statistical model. In these meth-ods, the noise signal is always assumed to satisfy and characterized by Gaussian distribution, while the assump-tion of noise does not always hold in practice and which causes that these kinds of method fail to distinguish speech from noise at low Signal-noise-ratio (SNR) level in non-stationary noise condition. For going further to improve the robustness of VAD, a enhanced speech based method is proposed. In the proposed method, the Laplacian distri-bution is used to model the remained noise since we find that the remained noise in enhanced speech satisfy Lapla-cian distribution; in addition, Gaussian mixture model is used to characterize the Discrete Fourier transform (DFT) coefficients of reconstructed speech in enhanced speech. Experimental results show that the proposed method per-forms better than the baseline method, especially in low SNR and non-stationary noise conditions. 相似文献
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Tian-Bo Deng 《IEEE transactions on circuits and systems. I, Regular papers》2005,52(5):932-942
Singular-value decomposition (SVD) can be efficiently utilized to obtain the optimal vector-array decomposition (VAD) for simplifying real-coefficient variable digital filter design problem, but the SVD-based VAD methods are not applicable to the design of complex-coefficient variable filters. This paper proposes a successive algorithm for decomposing arbitrary multidimensional complex array into the VAD form, and thus, a complex-coefficient variable digital filter with arbitrary variable frequency response can be easily obtained through constant complex-coefficient filter design and multidimensional polynomial fitting. The new VAD algorithm successively decomposes the complex array and its residual arrays into the vector-array pairs stage by stage, and each stage contains an iterative optimization that can be easily solved in a closed-form. Our computer simulations have demonstrated that the successive VAD converges very fast to the optimal solution. 相似文献
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本文提出了一种新的语音激活检测算法,这种方法基于竞争神经网络,主要应用了自组织特征映射网络并结合学习向量量化算法进行实现,并与其它神经网络算法进行了比较。该算法在多种噪声背景下具有较强的鲁棒性,仿真结果表明,这种基于竞争神经网络的算法优于ITU—T G.729B建议的算法。 相似文献
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基于盲源分离理论的麦克风阵列信号有音/无音检测方法 总被引:1,自引:0,他引:1
该文提出一种在方向性噪声场中多路麦克风信号同时进行有音/无音检测(VAD)的方法。在方向性噪声场中,由于各个麦克风接收信号中的噪声彼此之间相关,因而,可以利用盲源分离理论将方向噪声与语音源信号分离,从而获得相对比较纯净的语音源信号。对分离出的语音源信号进行有音/无音检测,获得VAD结果,同时估计出各个麦克风信号相对于该信号的时延值。以相对纯净语音源信号的VAD检测结果为参考,将其分别平移相应的时延值,即可同时获得多路麦克风信号的VAD结果。计算机模拟结果表明,在方向性噪声场的多种情况下,该方法对具有加性噪声的多路麦克风信号均具有较好的有音/无音检测能力。 相似文献
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Shota Morita Masashi Unoki Xugang Lu Masato Akagi 《Journal of Signal Processing Systems》2016,82(2):163-173
Voice activity detection (VAD) is used to detect speech and non-speech periods from observed speech signals. It is an important front-end technique for many speech technology applications. Many VAD methods have been proposed. However most of them have been applied under clean or noisy conditions. Only a few methods have been proposed for reverberant conditions, particularly under noisy reverberant conditions. We therefore need to understand the ill effects of noise and reverberation on speech to design an accurate and robust method of VAD under noisy reverberant conditions. The ill effects of noise and reverberation for speech can be regarded as the modulation transfer function (MTF) under noisy and reverberant conditions. Therefore, our study is based on the MTF concept to reduce the ill effects of noise and reverberation on speech, and propose a robust VAD method that we obtained in this study. Noise reduction and dereverberation were first applied to the temporal power envelope of the speech signal to restore the temporal power envelope with this method. Then, power thresholding as a VAD decision was designed based on the restored temporal power envelope. A method of estimating the signal to noise ratio (SNR) was proposed to accurately estimate the SNR in the noise reduction stage. Experiments under both artificial and realistic noisy reverberant conditions were carried out to evaluate the performance of the proposed method of VAD and it was compared with conventional VAD methods. The results revealed that the proposed method significantly outperformed the conventional methods under artificial and realistic noisy reverberant conditions. 相似文献