基于多窗谱的心理声学语音增强

与传统的周期谱图相比,多窗谱具有更小的估计方差。从含噪语音的多窗谱对噪声及噪声与含噪语音之比(NNSR)进行估计,用基于NNSR的幅度谱减实现用于计算人耳掩蔽阈值的预增强语音,用集成了人耳掩蔽阈值的心理声学加权规则实现最终的增强语音。考虑到多窗谱的特点对掩蔽偏移量进行了修正,修正后的重建语音,其客观测量指标修正巴克谱测度比修正前有一定的改进。再对心理声学加权规则作最大值小于1的限制,则输入信噪比越大(0 dB以上),分段信噪比和总体信噪比提高得越多。非正式试听表明重建语音失真较小,背景噪声大大降低,且没有音乐噪声。     

结合幅度谱和功率谱字典的语音增强方法

从双路字典学习、噪声功率谱估计、语音幅度谱重构角度提出了一种改进的谱特征稀疏表示语音增强方法。在字典学习阶段,融合功率谱与幅度谱特征,采用区分性字典降低语音字典和噪声字典的相干性;在语音增强阶段,提出一种噪声功率谱估计方法对非平稳噪声进行跟踪估计;考虑到幅度谱和功率谱特征对不同噪声的适应程度不同,设计了语音重构权值表。对分别由幅度谱和功率谱恢复而来的两路信号进行自适应加权重构,结合相位补偿函数得到增强后的语音信号。实验结果表明,该方法在平稳、非平稳噪声环境下相比于单一谱特征的语音增强方法平均提高31.6%,改善了语音增强方法的性能。     

结合幅度谱和功率谱字典的语音增强方法

从双路字典学习、噪声功率谱估计、语音幅度谱重构角度提出了一种改进的谱特征稀疏表示语音增强方法。在字典学习阶段,融合功率谱与幅度谱特征,采用区分性字典降低语音字典和噪声字典的相干性;在语音增强阶段,提出一种噪声功率谱估计方法对非平稳噪声进行跟踪估计;考虑到幅度谱和功率谱特征对不同噪声的适应程度不同,设计了语音重构权值表。对分别由幅度谱和功率谱恢复而来的两路信号进行自适应加权重构,结合相位补偿函数得到增强后的语音信号。实验结果表明,该方法在平稳、非平稳噪声环境下相比于单一谱特征的语音增强方法平均提高31.6%,改善了语音增强方法的性能。     

A two-stage spectrum sensing scheme based on energy detection and a novel multitaper method

Wideband spectrum sensing has drawn much attention in recent years since it provides more opportunities to the secondary users. However, wideband spectrum sensing requires a long time and a complex mechanism at the sensing terminal.A two-stage wideband spectrum sensing scheme is considered to proceed spectrum sensing with low time consumption and high performance to tackle this predicament. In this scheme, a novel multitaper spectrum sensing(MSS) method is proposed to mitigate the poor performance of energy detection(ED) in the low signal-to-noise ratio(SNR) region. The closed-form expression of the decision threshold is derived based on the Neyman–Pearson criterion and the probability of detection in the Rayleigh fading channel is analyzed. An optimization problem is formulated to maximize the probability of detection of the proposed two-stage scheme and the average sensing time of the two-stage scheme is analyzed. Numerical results validate the efficiency of MSS and show that the two-stage spectrum sensing scheme enjoys higher performance in the low SNR region and lower time cost in the high SNR region than the single-stage scheme.     

自适应平滑周期图语音增强研究

提出基于功率谱结构特征的频带间自适应平滑周期图,解决周期图估计的频率分辨率和方差的矛盾,并应用于语音增强算法的幅度谱减法。测试结果表明,自适应平滑周期图谱减法对于各种功率谱结构特征的噪声,在平均段信噪比提高、平均对数谱距离等性能指标上优于其它周期图估计方法的谱减法。     

Speech enhancement by array crosstalk resistant ANC and spectrum subtraction

Microphone array-based speech enhancement has great importance for speech communications and speech recognition. To reduce the aperture of the microphone array and to increase the effect of the speech enhancement will greatly broaden the application areas of the microphone array. An array crosstalk resistant adaptive noise cancellation method is therefore presented. And then an improved spectral subtraction algorithm is further cascaded to obtain better enhancement results. Theoretic analysis and experiments indicate that the proposed scheme needs only a very small microphone array while it simultaneously achieves a higher SNR improvement. Besides, the proposed scheme can be used in many noisy environments and is easy for real-time implementation.     

Adaptive signal enhancement for overlapped peaks based on weighting factor selection

Peak-sharpening is an effective method for the peak position detection of overlapped spectra. However, the weighing factor parameter strongly affects the sharpening performance, and the derivative adopted in the peak-sharpening method is sensitive to noise. In this paper, an adaptive peak-sharpening method based on weighting factor selection is proposed. The relationship between the sharpening ratio and weighting factor is studied. In addition, the Savitzky–Golay filter is adopted due to its excellent noise reduction and peak shape retention abilities. First, the smoothed signal and second-order derivative signal are obtained by the Savitzky–Golay filter. Then, the parameters of the overlapped peaks are estimated for the weighting factor selection. Next, the peak position is detected by the peak-sharpening method. After that step, the estimated parameters are updated, and the above steps are iterated until the detection of the peak position converges. Finally, the converged results are considered to be the final detection results. The experimental results using a simulated dataset, a virtual mass spectra dataset and a polarography dataset show that the proposed method is effective for peak position detection.     

改进的正交匹配追踪的语音增强算法*

为了提高传统正交匹配追踪（Orthogonal Matching Pursuit,OMP ）算法的语音增强性能和运算速度,本研究基于稀疏编码理论,提出了一种改进的OMP算法的语音增强算法。其一,将K-奇异值分解（K-singular value decomposition,K-SVD）算法与OMP算法相结合,通过设置能量阈值的方法,提高OMP算法的语音增强性能;其二,通过改进传统OMP算法中信号稀疏逼近的计算方法,提高算法的运算速度。改进的OMP算法的语音增强算法与传统K-SVD语音增强算法相比,采用PESQ评价增强语音的质量,NCM评价语音的可懂度。在NCM的值基本保持不变的情况下,PESQ的值平均提高约12.47%,取得了更好的增强效果。取得了更好的增强效果。改进的OMP算法的运算速度与传统OMP算法相比提高近一倍。     

Speech dereverberation method based on spectral subtraction and spectral line enhancement

Speech signals recorded with a distant microphone usually are interfered by the spatial reverberation in the room, which severely degrades the clarity and intelligibility of speech. A speech dereverberation method based on spectral subtraction and spectral line enhancement is proposed in this paper. Following the generalized statistical reverberation model, the power spectrum of late reverberation is estimated and removed from the reverberation speech by the spectral subtraction method. Then, according to the human auditory model, a spectral line enhancement technique based on adaptive post-filtering is adopted to further eliminate the reverberant components between adjacent speech formants. The proposed method can effectively suppress the spatial reverberation and improve the auditory perception of speech. The subjective and objective evaluation results reveal that the perceptual quality of speech is greatly improved by the proposed method.     

基于轻量级卷积门控循环神经网络的语音增强方法*

针对在基于深度学习语音增强的方法中因采用因果式的网络输入导致语音增强性能下降的问题,提出了一种基于轻量级卷积门控循环神经网络(LCGRU)的语音增强方法。门控循环神经网络能够建模语音信号的时间相关性,但是其全连接结构破坏了语音信号的时频结构特征,并且参数数量庞大,不利于网络的训练。对此,本文采用卷积核替代门控循环神经网络中的全连接结构,在对语音信号时间相关性建模的同时保留了语音信号的时频结构特征,同时降低了网络的参数数量。为充分利用先前帧的特征信息,该网络单元当前时刻的输入融合了上一时刻的输入与输出。针对网络训练过程中容易产生过拟合的问题,本文采用了线性门控机制来控制信息的传输,这缓解了网络训练过程中的过拟合问题,提高了网络的语音增强性能。实验结果表明,本文所提出的网络结构在增强后的语音感知质量(PESQ),语音短时客观可懂度(STOI),分段信噪比(SSNR)等指标上均优于传统的网络结构。     

基于阵列抗串扰自适应噪声抵消与谱相减相结合的语音增强

基于传声器阵的语音增强对于语音通信及语音识别具有重要意义,减小传声器阵的孔径并提高其语音增强的效果将十分有利于扩大其应用范围。为此给出一种阵列抗串扰自适应噪声抵消方法,并将其与改进的谱相减技术相结合。分析与实验表明,该方案使增强语音的信噪比有了较大提高,所需的阵列孔径很小,而且还适用于多种噪声环境,并易于实时实现。     

Speech enhancement based on the discrete Gabor transform and multi-notch adaptive digital filters

This paper presents a new method to speech enhancement based on time-frequency analysis and adaptive digital filtering. The proposed method for dual-channel speech enhancement was developed by tracking frequencies of corrupting signal by the discrete Gabor transform (DGT) and implementing multi-notch adaptive digital filter (MNADF) at those frequencies. Since no a priori knowledge of the noise source statistics is required this method differs from traditional speech enhancement methods. Specifically, the proposed method was applied to the case where speech quality and intelligibility deteriorate in the presence of background noise. Speech coders and automatic speech recognition (ASR) systems are designed to act on clean speech signals. Therefore, corrupted speech signals by the noise must be enhanced before their processing. The method uses a primary input containing the corrupted speech signal while a reference input containing the noise only. In this paper, we designed MNADF instead of single-notch adaptive digital filter and used DGT to track frequencies of corrupting signal because fast filtering process and fast measure of the time-dependent noise frequency are of great importance in speech enhancement process. Therefore, MNADF was implemented to take advantage of fast filtering process. Different types of noises from Noisex-92 database were used to degrade real speech signals. Objective measures, the study of the speech spectrograms and global signal-to-noise ratio (SNR), segmental SNR (segSNR), Itakura-Saito distance measure as well as subjective listing test demonstrated consistently superior enhancement performance of the proposed method over traditional speech enhancement method such as spectral subtraction. Combining MNADF and DGT, excellent speech enhancement was obtained.     

基于听觉掩蔽效应和Bark子波变换的语音增强

提出了一种适用于低信噪比下的提高语音的听觉效果的语音增强方法。该方法在谱减法的基础上有两个特点:首先减参数是根据人耳听觉掩蔽效应提出的且是自适应的;其次采用了与人耳听觉系统特性更为适应的Bark子波变换方法对增强前后的语音进行分析。对该算法进行了客观和主观测试,结果表明:与谱减法相比对低信噪比的语音信号,(1)能更好地抑制残留噪声和背景噪声,(2)增强后的语音具有更好的清晰度和可懂度。     

基于修正Mel域掩蔽模型和无语音概率的耳语音增强

提出了一种基于修正Mel域听觉掩蔽模型和无语音概率的耳语音增强方法。该方法根据耳语音的发音特点对Mel频率进行修正,对每一帧耳语音信号进行Mel域频带滤波,同时通过无语音概率(SAP)动态地确定每个频带的听觉掩蔽阈值,对不同的听觉掩蔽阈值自适应地调整谱减系数来进行耳语音增强。对增强后的耳语音进行客观和主观测试,结果表明,该方法与其它谱减法相比,能将残留噪声和背景噪声控制在人耳掩蔽阈值下,取得更小的语音失真,主观听觉也得到了很大的改善。     

Speech enhancement method based on low-rank approximation in a reproducing kernel Hilbert space

Speech signal is corrupted unavoidably by noisy environment in subway, factory, and restaurant or speech from other speakers in speech communication. Speech enhancement methods have been widely studied to minimize noise influence in different linear transform domain, such as discrete Fourier transform domain, Karhunen-Loeve transform domain or discrete cosine transform domain. Kernel method as a nonlinear transform has received a lot of interest recently and is commonly used in many applications including audio signal processing. However this kind of method typically suffers from the computational complexity. In this paper, we propose a speech enhancement algorithm using low-rank approximation in a reproducing kernel Hilbert space to reduce storage space and running time with very little performance loss in the enhanced speech. We also analyze the root mean squared error bound between the enhanced vectors obtained by the approximation kernel matrix and the full kernel matrix. Simulations show that the proposed method can improve the computation speed of the algorithm with the approximate performance compared with that of the full kernel matrix.     

增强颗粒散射中缺陷回波信号的相关加权分离谱方法

粗晶材料的超声无损检测受背散射的影响,使得其信噪比很低,且这里的噪声是与发射超声波相干的噪声,不能用简单的时间平均来消除。分离谱技术已被证明是一种抑制背散射信号、提高信噪比的良好方法,许多人为此发展了各种理论作为分离谱的后处理算法。本文介绍了一种增强超声回波信号的相关加权前处理算法。这里,取自粗晶材料标准缺陷的窄脉冲超声回波被定义为标准子波,利用子波与超声检测信号的互相关作为权系数对检测信号进行加权,此技术与分离谱处理结合起来能使提高信噪比的性能更优。实验结果显示了本文所述方法可改善诸如奥氏体不锈钢一类粗晶材料超声检测的信噪比     

基于Laplace-Gauss模型和简化相位判别的离散余弦变换域语音增强

分析了理想情况下离散余弦变换域中语音信号增益,先验信噪比及后验信噪比之间的关系,用实际数据获得了各种信噪比下增益范围的统计特性。基于语音呈Laplace分布、噪声呈Gauss分布的模型,推导了具有相位特性的增益及先验信噪比的估计公式,通过合理性分析得到了简化的相位判别准则。实验结果表明,在高斯白噪声和F16飞机噪声情况下,简化的相位判别可使低信噪比下的语音增强系统的性能得到较大的改善。     

基于双向搜索方法的最小值控制递归平均语音增强算法

语音增强效果的提高,有赖于对噪声的准确估计和对噪声变化的及时跟踪与更新。为了提高对非平稳噪声的估计和更新能力,本文基于\     

Speech enhancement using the modified phase-opponency model

In this paper we present a model called the Modified Phase-Opponency (MPO) model for single-channel speech enhancement when the speech is corrupted by additive noise. The MPO model is based on the auditory PO model, proposed for detection of tones in noise. The PO model includes a physiologically realistic mechanism for processing the information in neural discharge times and exploits the frequency-dependent phase properties of the tuned filters in the auditory periphery by using a cross-auditory-nerve-fiber coincidence detection for extracting temporal cues. The MPO model alters the components of the PO model such that the basic functionality of the PO model is maintained but the properties of the model can be analyzed and modified independently. The MPO-based speech enhancement scheme does not need to estimate the noise characteristics nor does it assume that the noise satisfies any statistical model. The MPO technique leads to the lowest value of the LPC-based objective measures and the highest value of the perceptual evaluation of speech quality measure compared to other methods when the speech signals are corrupted by fluctuating noise. Combining the MPO speech enhancement technique with our aperiodicity, periodicity, and pitch detector further improves its performance.     

互相关自适应加权的医学超声成像算法研究

根据超声成像系统的超声回波信号互相关性,提出互相关自适应加权超声成像算法.该算法根据散射点回波信号之间的空间相关性设置加权系数,对不同位置的散射点进行自适应加权成像,从而降低了成像系统的旁瓣,抑制了相关性较差的噪声.通过Field II仿真的点目标和吸声斑目标处理结果表明该方法成像的横向和纵向分辨率高,成像速度快.相对于延时叠加(DAS)算法,该算法对散射点成像可提高对比度16 dB,对于吸声斑成像可提高对比度0.85 dB.最后采用完备数据集进行实验,结果表明该算法成像分辨率优于DAS算法,对比度提高了17 dB.