共查询到20条相似文献,搜索用时 171 毫秒
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基于多频带谱减法的抗噪声语音识别研究 总被引:1,自引:0,他引:1
为了减少在噪声环境下测试条件与训练条件不匹配导致的语音识别性能下降,提出了一种结合多频带谱减法的抗噪声语音识别系统。首先提取带噪语音的前几帧作为估计的噪声信号,将带噪语音、估计的噪声信号按频率划分M个互不相交的频带,然后根据每个频带内带噪语音与估计的噪声信号的性噪比,来确定该频带噪声的谱减参数。语音增强作为前端处理,与语音识别器级连构成抗噪声语音识别系统。通过实验仿真表明,基于多频带谱减法的抗噪声语音识别系统在不同信噪比不同类型的噪声下,识别性能明显优于基本谱减法。 相似文献
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非平稳噪声环境下的噪声估计算法 总被引:1,自引:0,他引:1
通过对噪音和语音频谱的分析,针对航空背景噪声的特性,提出一种用于语音增强的新的噪声估计算法。通常的噪声估计一般利用语音端点检测方法,取噪声段的谱平均值作为待估计的噪声谱,但该方法在信噪比较低时性能下降严重。笔者提出的基于频率段能量比的噪音谱估计方法,不依赖于语音端点检测而直接由语音帧来估计噪音谱,通过计算一帧语音中各频率段中能量比,以判断该帧是否含有语音来修正噪声谱估计的计算因子。算法提高了谱减法的适用范围,还在一般谱相减方法的基础上提出了改进的谱相减算法。 相似文献
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基于LPC美尔倒谱特征的带噪语音端点检测 总被引:2,自引:0,他引:2
复杂的噪声环境是语音识别系统在实际应用中性能下降的原因之一,识别预处理中的带噪端点检测作为关键技术,其性能的优劣某种程度上决定了识别率的高低。笔者提出了基于LPC美尔倒谱特征的带噪端点检测方法,对语音信号分高低频段分别提取IPC美尔倒谱特征分析,根据Mel倒谱距离判决,采用自适应噪声估计,实验结果表明,该方法计算效率较高,低信噪比下有较好的检测性能。 相似文献
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噪声鲁棒性是话者确认系统实用化的关键问题之一,本文设计了一种基于子带加权和GMM的话者确认系统,该系统将语音谱分为若干子带,采用基于短时能量分布的算法估计各子带噪声强度,并根据噪声强度来进行子带加权,最终生成具有更高鲁棒性的语音特征,语音识别模型采用简化的GMM.实验表明,上述方法能有效提高话者确认系统的性能,增强其噪声鲁棒性,而且在低噪声环境下,仍能保证系统性能不下降. 相似文献
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基于改进语音特征提取方法的语音识别 总被引:1,自引:1,他引:0
在分析语音特征提取方法基础上提出一种改进组合算法,并采用HMM声学模型和Viterbi算法进行模式训练和识别.实验结果表明,该算法在噪声环境中具有较好的鲁棒性,能有效提高噪声环境下中文连续语音识别的正确率,增强语音识别整体性能,因此在噪声环境下的语音识别系统中具有一定的实用价值. 相似文献
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噪声自适应的多数据流复合子带语音识别方法 总被引:3,自引:0,他引:3
首先针对现有丢失数据语音识别技术中的边缘化(marginalisation)技术在特征运用上的局限,提出了一种倒谱特征分量的可靠性估计方法,将边缘化技术推广到常用的倒谱语音识别系统中; 然后利用基于全带和子带倒谱特征的边缘化识别器在不同噪声中的互补性能,提出了一种噪声自适应的多数据流复合子带语音识别方法。实验结果表明,所提识别方法可以自适应地选出全带和子带数据流中受噪声影响较小者并以之为主要依据进行识别,有效地提高了识别系统在多变噪声环境中的鲁棒性。 相似文献
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Jeng-Ming Chen Bor-Sen Chen 《Signal Processing, IEEE Transactions on》2000,48(6):1548-1558
An investigation is undertaken to examine the parameter estimation problem of linear systems when some of the measurements are unavailable (i.e., missing data) and the probability of occurrence of missing data is unknown a priori. The system input and output data are also assumed to be corrupted by measurement noise, and the knowledge of the noise distribution is unknown. Under the unknown noise distribution and missing measurements, a consistent parameter estimation algorithm [which is based on an lp norm iterative estimation algorithm-iteratively reweighted least squares (IRLS)] is proposed to estimate the system parameters. We show that if the probability of missing measurement is less than one half, the parameter estimates via the proposed estimation algorithm will converge to the true parameters as the number of data tends to infinity. Finally, several simulation results are presented to illustrate the performance of the proposed l p norm iterative estimation algorithm. Simulation results indicate that under input/output missing data and noise environment, the proposed parameter estimation algorithm is an efficient approach toward the system parameter estimation problem 相似文献
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Direction-of-arrival (DOA) estimation is a central problem in array processing and has a variety of applications. In this paper, a new algorithm for finding DOAs of multiple temporally correlated signals is devised. The proposed approach is based on the joint diagonalization structure of a set of spatio-temporal correlation matrices. Unlike the subspace-based DOA estimators, it is not necessary to estimate the noise or signal subspace explicitly. Moreover, the proposed method can provide the spatial spectrum and estimate the DOAs even when the number of sources is not known a priori. Interestingly, it is revealed that the well-known MUSIC method is a special case of our algorithm. Simulation results validate that the developed approach is superior to conventional DOA estimators in terms of resolution capability, estimation accuracy, and robustness against array model errors. 相似文献
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As the power spectrum density of direct sequence spread spectrum (DS/SS or DS), signals is often much lower than the noises’, the DS signals have the ability to resist interception and interference. But in point of fact, if we have get some parameters of the DS signals, including information symbol period, chip period of the pseudo noise (PN) sequence, we can estimate the PN sequence in DS signals blindly, this has great value for management or interception of DS communications. Ref.[1… 相似文献
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为了解决Alpha稳定分布噪声环境下运动舰船目标的长度估计问题,该文借鉴非线性变换抑制脉冲噪声以及多普勒目标运动特性估计思想,提出基于广义时频分析(G-TFA)和最小二乘估计的运动目标长度估计方法。该方法首先利用G-TFA获取Alpha稳定分布噪声环境下运动目标的多普勒频率,然后利用最小二乘方法估计出目标航速和不同位置的横正时刻,最后利用上述估计结果计算目标长度。以广义Winger-Ville分布(G-WVD)为例,从理论上推导了G-TFA在Alpha稳定分布噪声环境下具有提取目标多普勒特征的能力,并通过仿真实验验证了该算法在中低混合信噪比下的稳健性。与现有算法相比,该文所提算法不需要估计噪声特征指数,算法性能优于基于传统时频分析的估计方法。 相似文献
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Sadler B.M. Giannakis G.B. Keh-Shin Lii 《Signal Processing, IEEE Transactions on》1994,42(10):2729-2741
One of the primary applications of higher order statistics has been for detection and estimation of nonGaussian signals in Gaussian noise of unknown covariance. This is motivated by the fact that higher order cumulants of Gaussian processes vanish. We study the opposite problem, namely, detection and estimation in nonGaussian noise. We estimate cumulants of nonGaussian processes in the presence of unknown deterministic and/or Gaussian signals, which allows either parametric or nonparametric estimation of the covariance of the nonGaussian noise. Our approach is to augment existing second-order detection methods using cumulants. We propose solutions for detection of deterministic signals based on matched filters and the generalized likelihood ratio test which incorporate cumulants, where the resulting solutions are valid under either detection hypotheses. This allows for single record detection and obviates the need for noise-only training records. The problem of estimating signal strength in the presence of nonGaussian noise of unknown covariance is also considered, and a cumulant-based solution is proposed which uses a single data record. Examples are used throughout to illustrate our proposed methods 相似文献
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The estimation of dynamically evolving ellipsoids from noisy lower-dimensional projections is examined. In particular, this work describes a model-based approach using geometric reconstruction and recursive estimation techniques to obtain a dynamic estimate of left-ventricular ejection fraction from a gated set of planar myocardial perfusion images. The proposed approach differs from current ejection fraction estimation techniques both in the imaging modality used and in the subsequent processing which yields a dynamic ejection fraction estimate. For this work, the left ventricle is modeled as a dynamically evolving three-dimensional (3-D) ellipsoid. The left-ventricular outline observed in the myocardial perfusion images is then modeled as a dynamic, two-dimensional (2-D) ellipsoid, obtained as the projection of the former 3-D ellipsoid. This data is processed in two ways: first, as a 3-D dynamic ellipsoid reconstruction problem; second, each view is considered as a 2-D dynamic ellipse estimation problem and then the 3-D ejection fraction is obtained by combining the effective 2-D ejection fractions of each view. The approximating ellipsoids are reconstructed using a Rauch-Tung-Striebel smoothing filter, which produces an ejection fraction estimate that is more robust to noise since it is based on the entire data set; in contrast, traditional ejection fraction estimates are based only on true frames of data. Further, numerical studies of the sensitivity of this approach to unknown dynamics and projection geometry are presented, providing a rational basis for specifying system parameters. This investigation includes estimation of ejection fraction from both simulated and real data. 相似文献
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Bearing estimation algorithms based on the cumulants of array data have been developed to suppress additive spatially correlated Gaussian noises. In practice, however, the noises encountered in signal processing environments are often non-Gaussian, and the applications of those cumulant-based algorithms designed for Gaussian noise to non-Gaussian environments may severely degrade the estimation performance. The authors propose a new cumulant-based method to solve this problem. This approach is based on the fourth-order cumulants of the array data transformed by DFT, and relies on the statistical central limit theorem to show that the fourth-order cumulants of the additive non-Gaussian noises approach zero in each DFT cell. Simulation results are presented to demonstrate that the proposed method can effectively estimate the bearings in both Gaussian and non-Gaussian noise environments 相似文献
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A new approach for active-sonar target detection and bearing estimation from a mobile two-dimensional array of sensors operating in a predominantly noisy environment is presented. Sensor-level adaptive noise cancellation featuring an unconventional method for reference-noise estimation is the key preprocessing step in the proposed approach. A signal-subspace algorithm resulting from two-stage optimisation based on a generalised eigendecomposition of the signal plus (residual) noise covariance matrix is employed to estimate the bearing of the detected target. Simulation results conclusively demonstrate that the proposed scheme is capable of performing target detection and the subsequent two-dimensional bearing estimation with a high degree of reliability at signal-to-noise power ratios as low as -70 and -40-dB, respectively. 相似文献