SPEECH ENHANCEMENT USING CONSTRAINED SPECTRAL AMPLITUDE SUBTRACTION BASED ON NONCAUSAL A PRIORI SNR

Two gain forms of spectral amplitude subtraction are derived theoretically without neglecting the correlation of speech and noise spectrum during the period of a fralne. In the implementation, the constrained gain is expressed as a function of noncausal a priori SNR （Signal-to-Noise Ratio）. Noise and noncausal a priori SNR are estimated from the multitaper spectrum of the noisy signal with algorithms modified to be suitable for the multitaper spectruln. Objective evaluations show that in case of white Gaussian noise the proposed method outperforms some methods based on LSA （Log Spectral Amplitude） in terms of MBSD （Modified Bark Spectral Distortion）, segmental SNR and overall SNR, and informal listening tests show that speech reconstructed in this way has little speech distortion and musical noise is nearly inaudible even at low SNR.

Noise Removal in Speech Processing Using Spectral Subtraction

Spectral subtraction is used in this research as a method to remove noise from noisy speech signals in the frequency domain. This method consists of computing the spectrum of the noisy speech using the Fast Fourier Transform (FFT) and subtracting the average magnitude of the noise spectrum from the noisy speech spectrum. We applied spectral subtraction to the speech signal “Real graph”. A digital audio recorder system embedded in a personal computer was used to sample the speech signal “Real graph” to which we digitally added vacuum cleaner noise. The noise removal algorithm was implemented using Matlab software by storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse Fast Fourier Transform (IFFT). The performance of the algorithm was evaluated by calculating the Speech to Noise Ratio (SNR). Frame averaging was introduced as an optional technique that could improve the SNR. Seventeen different configurations with various lengths of the Hanning time windows, various degrees of data buffers overlapping, and various numbers of frames to be averaged were investigated in view of improving the SNR. Results showed that using one-fourth overlapped data buffers with 128 points Hanning windows and no frames averaging leads to the best performance in removing noise from the noisy speech.

Single-Channel Speech Enhancement Based on Improved Frame-Iterative Spectral Subtraction in the Modulation Domain

Aiming at the problem of music noise introduced by classical spectral subtraction,a shorttime modulation domain(STM)spectral subtraction method has been successfully applied for singlechannel speech enhancement.However,due to the inaccurate voice activity detection(VAD),the residual music noise and enhanced performance still need to be further improved,especially in the low signal to noise ratio(SNR)scenarios.To address this issue,an improved frame iterative spectral subtraction in the STM domain(IMModSSub)is proposed.More specifically,with the inter-frame correlation,the noise subtraction is directly applied to handle the noisy signal for each frame in the STM domain.Then,the noisy signal is classified into speech or silence frames based on a predefined threshold of segmented SNR.With these classification results,a corresponding mask function is developed for noisy speech after noise subtraction.Finally,exploiting the increased sparsity of speech signal in the modulation domain,the orthogonal matching pursuit(OMP)technique is employed to the speech frames for improving the speech quality and intelligibility.The effectiveness of the proposed method is evaluated with three types of noise,including white noise,pink noise,and hfchannel noise.The obtained results show that the proposed method outperforms some established baselines at lower SNRs(-5 to +5 dB).

Single-Channel Speech Enhancement Using Critical-Band Rate Scale Based Improved Multi-Band Spectral Subtraction

This paper addresses the problem of single-channel speech enhancement in the adverse environment. The critical-band rate scale based on improved multi-band spectral subtraction is investigated in this study for enhancement of single-channel speech. In this work, the whole speech spectrum is divided into different non-uniformly spaced frequency bands in accordance with the critical-band rate scale of the psycho-acoustic model and the spectral over-subtraction is carried-out separately in each band. In addition, for the estimation of the noise from each band, the adaptive noise estimation approach is used and does not require explicit speech silence detection. The noise is estimated and updated by adaptively smoothing the noisy signal power in each band. The smoothing parameter is controlled by a-posteriori signal-to-noise ratio (SNR). For the performance analysis of the proposed algorithm, the objective measures, such as, SNR, segmental SNR, and perceptual evaluations of the speech quality are conducted for the variety of noises at different levels of SNRs. The speech spectrogram and objective evaluations of the proposed algorithm are compared with other standard speech enhancement algorithms and proved that the musical structure of the remnant noise and background noise is better suppressed by the proposed algorithm.

NON-LINEAR SPECTRAL IMAGING MICROSCOPY STUDIES OF HUMAN HYPERTROPHIC SCAR

Skin scar is unique to humans,the major significant negative outcome sustained after thermal injuries,traumatic injuries,and surgical procedures.Hypertrophic scar in human skin is investigated using non-linear spectral imaging microscopy.The high contrast images and spectroscopic intensities of collagen and elastic fibers extracted from the spectral imaging of normal skin tissue,and the normal skin near and far away from the hypertrophic scar tissues in a 10-year-old patient case are obtained.The results show that there are apparent differences in the morphological structure and spectral characteristics of collagen and elastic fibers when comparing the normal skin with the hypertrophic scar tissue.These differences can be good indicators to differentiate the normal skin and hypertrophic scar tissue and demonstrate that non-linear spectral imaging microscopy has potential to noninvasively investigate the pathophysiology of human hypertrophic scar.

Application of gemstone spectral imaging for efficacy evaluation in hepatocellular carcinoma after transarterial chemoembolization

AIM: To assess the value of gemstone spectral imaging (GSI) in efficacy evaluation in hepatocellular cancer (HCC) after transcatheter arterial chemoembolization (TACE) treatment.METHODS: Thirty patients with HCC underwent GSI, including nonenhanced, arterial, portalvenous and delayed phase scans, after TACE treatment. Arterial phase images were acquired with GSI for reconstruction of virtual nonenhanced images and color overlay images. Digital subtraction angiography (DSA) was performed in all these patients. Two blinded and independent readers evaluated the data in two reading sessions; standard nonenhanced, arterial, portalvenous, and delayed phase images were read in session A, and the optimal monochromatic images, iodine/water based images and spectrum features were read in session B. Sensitivity and specificity were calculated with the DSA data as the reference standard. The sensitivity and specificity were compared using the χ2 test.RESULTS: DSA revealed 154 lesions in 30 patients, and 100 of them had blood supply. Overall sensitivity and specificity were 72% (72/100) and 77.8% (42/54) for session A, and 97% (97/100) and 94.4% (51/54) for session B, respectively. The sensitivity and specificity of the two reading sessions were significantly different (χ2 = 23.04, χ2 = 7.11, P < 0.05).CONCLUSION: Compared with conventional CT, GSI could significantly improve the detection of small and multiple lesions without increasing the radiation dose. Based on spectrum features, GSI could assess tumor homogeneity and more accurately identify residual tumors and recurrent or metastatic lesions during efficacy evaluation and follow-up in HCC after TACE treatment.

On the Non-linear Motion of IGS Station

With the daily SINEX files of the IGS, the time series of IGS stations are obtained using an independently developed software under generalized network adjustment models with coordinate patterns. From the time series, non-linear motions are found. With spectral analysis method, the variation frequency （annual period and semi-annual period） of the site velocity is found. Moreover, the empirical model of the velocity variation of the station has been established by regression analysis method based on the weekly solution coordinate series of the station. With respect to the velocity of the IGS tracking station, it was better to model the variation periodically or to give a velocity periodically using a piece-wise linear function rather than a linear variable to estimate its bias.

基于机器学习的语音增强技术

主要研究基于机器学习的语音增强技术,以提升语音信号的质量。首先,介绍基于机器学习的语音增强系统框架。其次,详细探讨谱减法与深度神经网络(Deep Neural Network,DNN)相结合的语音增强方法的数学原理。最后,采用NOISEX-92数据集测试与评估提出的方法。实验结果表明,基于谱减法与DNN的语音增强方法在提升信噪比和语音清晰度方面取得显著的效果,能够有效提升语音通信质量。

改进粒子滤波跟踪的视听双模态语音识别仿真

噪声环境下视听语音不易被识别,为提升语音识别效果,提出改进粒子滤波跟踪的视听双模态语音识别方法。采用谱减法去除噪声数据,完成视听双模态语音的消噪处理;根据人语和唇动信息之间的相关性,采用改进粒子滤波跟踪方法提取视听双模态语音特征信息,构建transformer语音识别模型,将提取的特征信息输入到模型内实施并行训练,实现视听双模态语音的有效识别。实验结果表明,通过对上述方法开展信噪比测试、识别性能测试,验证了上述方法的可行性高、可靠性强。

肺音信号去噪技术的比较与分析

肺音听诊是医生诊断各种呼吸系统疾病的一种简单、无创的方法,可为检测和鉴别呼吸病理提供及时而有用的信息。然而,肺音中的噪声污染是采集肺音时不可避免的问题,在医生诊断患者时,噪声的存在会对肺音分析产生严重阻碍,因此有效去除多余噪声更有助于准确、客观地评价肺音信息。论文对不同去噪技术进行了讨论,包括小波去噪、最小均方自适应滤波、谱减法,评估了三种去噪性能指标,得出了相关比较性结论。

基于角度压制比谱减的环境自适应双麦语音增强

[目的]针对智能终端小型化、使用场景多样化的发展趋势,研制一种既能满足严苛的尺度、算力、存储空间限制,又能实现环境自适应的双麦语音增强算法.[方法]考虑到麦克风阵列波束形成算法可以增强期望方向信号,同时抑制非期望方向的噪声,但小尺寸阵列波束主瓣波束宽度较宽、影响增强效果.在小尺寸双麦对目标方向进行波束对准增强的基础上,参考干扰方向噪声,进一步对目标方向语音进行谱减处理,并引入角度压制比实时检测干扰方向噪声的能量估计,实现对不同混响、噪声类型的自适应处理,从而提升语音增强效果.[结果]角度压制比随混响时间增加而增大,与信噪比不相关.相对于原始带噪信号、滤波-累加波束形成(filter-and-sum beamforming, FSB)信号、FSB结合固定对向谱减的语音增强信号,通过FSB结合角度压制比自适应对向谱减得到的语音增强信号,在不同噪声类型、不同信噪比和不同混响时间下,均能得到最高的分段信噪比得分和大多数的最高客观语音质量评估得分.[结论]角度压制比能一定程度地反映不同的混响情况,利用角度压制比得到的谱减阈值具有一定的环境适应性.

一种自适应的地面震动信号降噪方法

针对传统谱减法应对地面震动信号的干扰和噪声时降噪效果差、信号失真的问题,研究和分析不同类型干扰和噪声的功率谱特性,提出一种基于干扰和噪声的功率谱特性自适应选择降噪处理操作的方法,既满足了不同干扰和噪声的降噪要求,也避免了降噪处理造成的信号失真。

基于MobileNetV3卷积神经网络的供水管道漏损音频分类

为了对城市供水管网漏损音进行准确识别,提出一种基于MobileNetV3的供水管道漏损音频分类识别方法。首先将ROPP数据集中的音频文件进行离线数据增强,将漏损信号转变为对数梅尔谱图并采用谱减法实现数据降噪;然后使用注意力机制模块与MobileNetV3网络训练识别并提取图像特征;最后使用Softmax函数对漏损音频进行分类。实验结果表明,该方法可以使漏水类别的分类精确度达到99.40%,召回率达到99.20%。

基于梅尔频率倒谱系数的语音清晰度DRT识别

语音清晰度在通信终端、设备系统语音识别方面具有重要意义。本文对110dB噪声干扰下采集到的语音信号进行谱减法降噪,双门限端点检测提取发音字段,然后提取梅尔频率倒谱系数(MFCC),再将其进行差分计算,得到一阶和二阶分量,结合短时能量作为语音信号的特征参数,最后通过动态时间归整(DTW)进行相似度识别。实验表明,本文算法对汉语清晰度诊断押韵测试(DRT)字表的测试结果高达92.90%,有良好的识别率。

强噪环境下分频段数字音频信号精细化采集研究

在强噪环境下,为了获取高质量、低失真度的数字音频信号,提出一种强噪环境下分频段数字音频信号精细化采集方法。通过麦克风设备获取音频信号,利用LM4550芯片对其作采样、编码等处理后生成数字音频信号,基于AC-97单元接收数字音频信号,利用频段分割器对其作频带分解,获得互不重叠子频段数字音频信号。采用多窗谱谱减法对其去噪,利用数据通信接口将其传输给LM4550芯片,在完成模拟信号转换后,通过耳机输出,实现数字音频信号精细化采集。实验结果表明,该方法处理后的各子频段数字音频信号有用信息得以完整保留,并提高了信号波形的规整度和规律性,强噪声环境下数字音频信号的PESQ指标达到4.08以上,最大失真度为3.74%。

基于声学检测技术的刀片储能锂电池热失控行为研究

随着电化学储能的规模化应用,开展储能锂电池热失控检测与及时预警研究对于保障储能电站的安全运行具有重要意义。搭建储能锂电池单体热失控性能检测试验平台,对刀片储能锂电池进行热失控条件下的声音信号检测,分析电池鼓包、泄压阀打开、泄气、爆炸起火等热失控不同发展阶段的声音特性与变化规律,表明声音特征对于热失控检测的有效性。针对热失控声音特征易受储能电池舱内通风散热风机、储能变流器等其他设备噪声干扰的问题,提出基于小波包分解与谱减法语音增强的热失控声音信号抗干扰分析方法。分析结果表明,所提出的方法能够有效剔除热失控声音信号中的风噪干扰,可准确还原出电池热失控声音特征,还原声信号与实际声信号相似系数达到0.96,为储能锂电池热失控声音检测与早期预警提供技术参考。

一种改进的自适应子带谱熵语音端点检测方法

噪声环境下的语音端点检测在稳健语音识别中占有十分重要的地位。自适应子带谱熵法是一种新的端点检测方法,它的思想是将一帧语音分成若干个子带,再用谱熵法进行运算,子带的个数可以自适应选择。该方法具有一定的稳健性,但随着信噪比的降低,语音端点检测的准确性也随之下降。提出了一种结合加权功率谱减的子带自适应谱熵法,并给出了该方法的实现步骤。该方法采用边降噪边用稳健性好的特征参数做语音端点检测,从两个方面来提高端点检测的准确性。实验结果表明该方法具有良好的性能,在不同信噪比的不同加性噪声下系统识别率都有提高。

特高压交流变电站可听噪声分离方法

特高压交流变电站内主要设备产生的噪声相互混叠,增加了变电站噪声测试难度。因而提出了一种特高压交流变电站可听噪声分离方法,将变电站噪声分为变压器与电抗器本体噪声、冷却装置噪声以及电晕噪声。并利用小波包分析方法结合谱减法语音增强技术滤除环境噪声,分别设计通带与阻带梳状滤波器提取本体噪声信号以及包含变压器冷却装置与电晕噪声的混合声信号,然后根据电晕噪声的短时脉冲特性以及变压器冷却装置噪声的平稳性,利用谱减法语音增强技术分离出电晕噪声及变压器冷却装置噪声。研究结果表明:本体噪声、冷却装置噪声以及电晕噪声存在混叠现象;所分离出的本体噪声主要集中在100 Hz、200 Hz以及300 Hz三个窄带频率上;分离后的冷却装置噪声较为平稳,主要位于2 k Hz频率范围内;分离后的电晕噪声为宽频带噪声,具有短时脉冲性,分离前后脉冲信号的发生时间及幅值一致。对所分离噪声信号的时频特性分析结果证明了该方法的有效性。

基于频谱方差和谱减法的语音端点检测新算法

针对低信噪比情况下频谱方差法对语音信号进行端点检测时准确率降低的问题,提出了一种结合频谱方差和谱减法的语音端点检测新算法。算法采用改进的谱减法对语音信号进行动态降噪处理,并依据得到的降噪后信号的频谱方差设置双门限值进行端点检测。仿真实验表明,该方法具有抗噪性好、自适应性强等优点,在低信噪比情况下检测的准确率与普通的频谱方差法相比有很大的提高。

改进的语音端点检测技术

为了提高低信噪比下语音端点检测的性能,提出了一种改进的基于谱减法和自适应子带谱熵的语音端点检测方法。该方法先利用谱减法对带噪语音消除加性噪声,及时更新背景噪声估计,再对增强后的语音信号利用改进的自适应子带谱熵进行端点检测。实验结果表明,该方法具有良好的检测性能,相对传统方法提高了端点检测的准确率,在低信噪比环境下仍能比较准确地检测到语音的端点。