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.

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.

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.

Spectral DMC-30SS

在某种意义上，Spectral也是一个追求精益求精的品牌，它不会频繁推出新型号产品，却会根据最新的技术研发成果，将先前已经达到的水准再向前做提高。这种进步的取得，无疑极大地提升了该品牌在高端用户中的受关注度。很多时候，一部优秀的前级牵涉到的方方面面实在是非常多，而在这其中，有些因素是技术所决定的感觉。这方面的例子有很多。譬如说，我们通常都会在聆听、评鉴一套系统的时候谈论器材的速度问题。对于前级来讲，速度问题也是一个相当关键化的问题，因为这涉及到其与其他器材的搭配及其还原风格问题。一直以来，Spectral都是一个比较强调前级应该有较快速度特性的品牌。

基于FSS与PLP的噪声鲁棒语音识别

提出了一种基于分数阶谱相减(FSS)与感知线性预测(PLP)相结合的噪声鲁棒语音识别方法,记为FSS+PLPC。该方法首先通过FSS在分数阶Fourier域对带噪语音进行降噪处理,然后计算增强语音的均方误差和Itakura距离并进行比较,以获得FSS的近似最优分数阶阶数。最后对根据此阶数得到的增强语音提取感知线性预测倒谱(PLPC)。实验结果表明,FSS+PLPC对于数字语音的识别性能优于传统的谱减法(SS+PLPC)和感知线性预测倒谱(PLPC)法,并且随着信噪比的降低FSS+PLPC表现出较好的噪声鲁棒性。

融合SS、MFCC和PMC技术的语音去噪方法

为提高语音识别系统在噪音情况下的识别率,提出了一种融合信号级去噪、参数级去噪、模型级去噪的方法.首先用谱减法对带噪的语音信号进行去噪,再利用Mel倒谱系数(MFCC)对处理后的语音信号进行特征提取,最后经过并行模型结合处理法(PMC)处理得到较高识别率的语音信号.

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.

AdaBoost for Improved Voice-Band Signal Classification

A good voice-band signal classification can not only enable the safe application of speech ceding techniques, the implementation of a Digital Signal Interpolation （DSI） system, but also facilitate network administration and planning by providing accurate voice-band traffic analysis. A new method is proposed to detect and classify the presence of various voice-band signals on the General Switched Telephone Network （GSTN）. The method uses a combination of simple base classifiers through the AdaBoost algorithm. The conventional classification features for voice- band data classification are combined and optimized by the AdaBoost algorithm and spectral subtraction method. Experiments show the simpleness, effectiveness, efficiency and flexibility of the method.

Application of Spectral Analysis in Multichannel Digital Filter

The paper proposes a multichannel digital filtering method for signal, discusses the application of spectral analysis in the method, and introduces an improved fast transformation formula for Fourier forward and inverse transformation.

A Multi-Band Speech Enhancement Algorithm Exploiting Iterative Processing for Enhancement of Single Channel Speech

This paper proposes a multi-band speech enhancement algorithm exploiting iterative processing for enhancement of single channel speech. In the proposed algorithm, the output of the multi-band spectral subtraction (MBSS) algorithm is used as the input signal again for next iteration process. As after the first MBSS processing step, the additive noise transforms to the remnant noise, the remnant noise needs to be further re-estimated. The proposed algorithm reduces the remnant musical noise further by iterating the enhanced output signal to the input again and performing the operation repeatedly. The newly estimated remnant noise is further used to process the next MBSS step. This procedure is iterated a small number of times. The proposed algorithm estimates noise in each iteration and spectral over-subtraction is executed independently in each band. The experiments are conducted for various types of noises. The performance of the proposed enhancement algorithm is evaluated for various types of noises at different level of SNRs using, 1) objective quality measures: signal-to-noise ratio (SNR), segmental SNR, perceptual evaluation of speech quality (PESQ);and 2) subjective quality measure: mean opinion score (MOS). The results of proposed enhancement algorithm are compared with the popular MBSS algorithm. Experimental results as well as the objective and subjective quality measurement test results confirm that the enhanced speech obtained from the proposed algorithm is more pleasant to listeners than speech enhanced by classical MBSS algorithm.

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。