双正交小波变换的算子矩阵及去相关性能分析

利用基于推选体制（Lifting Scheme)的小波变换实现算法,构造出了与具有紧支集的双正交小波滤波器对应的小波变换和逆小波变换的算子矩阵,这些矩阵是大小与信号长度相等的可逆常数矩阵。这些矩阵为从理论的角度研究双正交小波滤波器的性能提供了一个工具。利用这些矩阵,从理论的角度对不同的双正交小波滤波器的去相关性能进行了分析比较研究,并和其他常用的正交变换,如DCT、DFT进行比较,给出了定量的比较结果。比较结果表明,在高压缩比的情况下,（9,7）滤波器具有好的性能,而在中等压缩比下,（5,3）滤波器有比较好的性能。     

An effective and objective criterion for evaluating the performance of denoising filters

Denoising filters are useful for reducing noise; however, they often blur and smear the edges and boundaries, which are necessary for segmenting or locating the objects. In order to overcome above problem, many filters with contrast enhancement capability have been developed, and they have wide applications in related fields. Recently, researchers found that the traditional criteria, such as mean squared error (MSE), signal-to-noise ratio (SNR), are not suitable for evaluating such filters.Due to lack of effective metrics for such tasks, visual inspection by human and some newly proposed image quality assessment (QA) criteria, such as structural similarity (SSIM) index are utilized. However, visual inspection depends on the subjectivity of observers heavily.This paper has proved that evaluating denoising filters is different from image quality assessment, i.e., existing image quality assessment criteria cannot effectively evaluate the performance of denoising filters, especially, of the filters having contrast enhancement capability; and new criteria should be established. Further, it proposes a novel objective and effective assessment criterion, homogeneity mean difference (HMD), to evaluate the performance of the filters since it can describe the textual and structural information and/or the changes in textual and structural information well. We have employed 503 images from three databases to demonstrate the superiority of the proposed metric over the existing ones, and to prove that HMD is an effective and useful metric for assessing denoising filters with/without contrast enhancement, which may find wide applications in image processing and computer vision.     

Noise variance estimation based on dual-channel phase difference for speech enhancement

In this paper, we propose a method for estimating a signal-to-noise ratio (SNR) in order to improve the performance of a dual-microphone speech enhancement algorithm. The proposed method is able to reliably estimate both a priori and a posteriori SNRs by exploring a direction-of-arrival (DOA)-based local SNR that is defined by using spatial cues obtained from dual-microphone signals. The estimated a priori and a posteriori SNRs are then incorporated into a Wiener filter. Consequently, it is shown from an objective perceptual evaluation of speech quality (PESQ) comparison and a subjective listening test that a speech enhancement algorithm employing the proposed SNR estimate outperforms those using conventional single- or dual-microphone speech enhancement algorithms such as the Wiener filter, beamformer, or phase error-based filter under different noise conditions ranging from 0 to 20 dB.     

A new method of speech transmission over space time block coded co-operative MIMO–OFDM networks using time and space diversity

Reliable and good quality of service for speech transmission over wireless network has been a major challenge for the communication engineers and researchers. In this paper a new technique of speech compression and transmission using different Daubechies wavelets in a space time block coded co-corporative MIMO–OFDM networks using time and space diversity has been proposed. The main focus has been laid on design and development of wavelet based compression of multimedia signals for cooperative MIMO–OFDM system. We tried to find out various major issues regarding the wavelet compression of a speech signal. These issues include choice of a wavelet, decomposition level and thresholding criteria suitable for speech compression and transmission in co-operative MIMO–OFDM systems. A wavelet based speech compression technique using hard and soft thresholding algorithm has been proposed. The work shows that wavelet compression with QPSK modulation is a promising compression technique in a cooperative MIMO–OFDM system which makes use of the elegant theory of wavelets. The performance has been evaluated using mean square error, peak signal to noise ratio, compression ratio, bit error rate, and retained signal energy. It has been found that the transmitted speech signal is retrieved well under noisy conditions at higher order Daubechies wavelets. From the results it is clear that proposed technique aims at a radio access technology that can provide service performance comparable to that of current fixed Line accesses. To evaluate the performance of the proposed method, various performance parameters have been compared with previously implemented techniques and it has been found that the proposed work shows better performance as compared to the already existing techniques.     

Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy

Dysfluency and stuttering are a break or interruption of normal speech such as repetition, prolongation, interjection of syllables, sounds, words or phrases and involuntary silent pauses or blocks in communication. Stuttering assessment through manual classification of speech dysfluencies is subjective, inconsistent, time consuming and prone to error. This paper proposes an objective evaluation of speech dysfluencies based on the wavelet packet transform with sample entropy features. Dysfluent speech signals are decomposed into six levels by using wavelet packet transform. Sample entropy (SampEn) features are extracted at every level of decomposition and they are used as features to characterize the speech dysfluencies (stuttered events). Three different classifiers such as k-nearest neighbor (kNN), linear discriminant analysis (LDA) based classifier and support vector machine (SVM) are used to investigate the performance of the sample entropy features for the classification of speech dysfluencies. 10-fold cross validation method is used for testing the reliability of the classifier results. The effect of different wavelet families on the classification performance is also performed. Experimental results demonstrate that the proposed features and classification algorithms give very promising classification accuracy of 96.67% with the standard deviation of 0.37 and also that the proposed method can be used to help speech language pathologist in classifying speech dysfluencies.     

Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction

This paper presents the comparative study of various wavelet filter based denoising methods according to different thresholding values applied to ultrasound images. An original image is transformed into a multi scale wavelet domain and the wavelet coefficients are processed by a soft thresholding method. The denoised image is the output image obtained from the inverse wavelet transform of the threshold coefficients using Donoho's method. It has been observed that such denoising methods are effective in the sense that they preserve the edge details besides suppressing the noise. The comparative evaluation of the denoising performance is shown using statistical significance tests for different wavelet filters. Image quality parameters such as peak signal-to-noise ratio, normalized mean square error, and correlation coefficient have been used to evaluate the performance of wavelet filters. The performance has also been compared with the adaptive weighted median filtering method.     

基于小波熵自适应阈值的语音信号去噪新方法

针对语音信号去噪问题, 提出小波熵自适应阈值去噪法。首先利用小波变换分解带噪语音信号, 计算小波分解后信号子带区间的小波熵, 然后将小波熵和自适应阈值相结合确定各层高频系数的阈值门限, 采用折中指数阈值函数对各层高频系数进行去噪处理, 重构降噪后的语音信号, 最后对比小波熵自适应阈值、极大极小阈值、固定阈值和无偏风险阈值去噪方法的性能。实验结果表明, 当输入信噪比为5 dB时, 小波熵自适应阈值去噪法的输出信噪比是最大的, 且其输入输出信噪比曲线高于其他三种阈值去噪法的输入输出信噪比曲线, 从而证实该算法具有更好的去噪性能。     

Reducing over- and under-estimation of the a priori SNR in speech enhancement techniques

Most speech enhancement methods based on short-time spectral modification are generally expressed as a spectral gain depending on the estimate of the local signal-to-noise ratio (SNR) on each frequency bin. Several studies have analyzed the performance of a priori SNR estimation algorithms to improve speech quality and to reduce speech distortions. In this paper, we concentrate on the analysis of over- and under estimation of the a priori SNR in speech enhancement and noise reduction systems. We first show that conventional approaches such as the decision-directed approach proposed by Ephraïm and Malah lead to a biased estimator for the a priori SNR. To reduce this bias, our strategy relies on the introduction of a correction term in the a priori SNR estimate depending on the current state of both the available a posteriori SNR and the estimated a priori one. The proposed solution leads to a bias-compensated a priori SNR estimate, and allows to finely estimating the output speech signal to be very close to the original one on each frequency bin. Such refinement procedure in the a priori SNR estimate can be inserted in any type of spectral gain function to improve the output speech quality. Objective tests under various environments in terms of the Normalized Covariance Metric (NCM) criterion, the Coherence Speech Intelligibility Index (CSII) criterion, the segmental SNR criterion and the Perceptual Evaluation of Speech Quality (PESQ) measure are presented showing the superiority of the proposed method compared to competitive algorithms.     

Joint variable frame rate and length analysis for speech recognition under adverse conditions

This paper presents a method that combines variable frame length and rate analysis for speech recognition in noisy environments, together with an investigation of the effect of different frame lengths on speech recognition performance. The method adopts frame selection using an a posteriori signal-to-noise (SNR) ratio weighted energy distance and increases the length of the selected frames, according to the number of non-selected preceding frames. It assigns a higher frame rate and a normal frame length to a rapidly changing and high SNR region of a speech signal, and a lower frame rate and an increased frame length to a steady or low SNR region. The speech recognition results show that the proposed variable frame rate and length method outperforms fixed frame rate and length analysis, as well as standalone variable frame rate analysis in terms of noise-robustness.     

引入神经网络中间神经元的快速小波图像压缩

目的针对自组织特征映射(SOFM)算法会出现严重的分块现象和快速小波变换在高压缩比的情况下图像恢复质量差的问题,提出引入神经网络中间神经元(relay neurons)的RSOFM-C矢量量化算法。方法引入了中间神经元的概念,使用中间神经元有效解决了码字利用不均匀的问题,并在神经网络中间层给出了欧氏距离不等式判据,排除不满足失真测度的神经元,减少重复计算,加快学习速度。根据差分脉冲编码调制(DPCM)中的差值信号编码原理将RSOFM-C算法与快速小波变换结合,使用RSOFM-C算法对由快速小波变换得到的图像低频信号进一步压缩。结果在仿真实验中,将本文算法与同类压缩方法进行对比,当压缩比为52%时,本文算法的峰值信噪比(PSNR)达到了39.28 d B,远远高于其他方法。结果表明,本文的压缩算法消除了分块现象,并且在保证高压缩比的同时获得高质量的重构图像。结论实验结果表明,本文提出的引入了中间神经元的快速小波压缩方法,具有高压缩比、高保真、速度快等优点,可以高效地压缩图像。     

A new a priori SNR estimator based on multiple linear regression technique for speech enhancement

We propose a new approach to estimate the a priori signal-to-noise ratio (SNR) based on a multiple linear regression (MLR) technique. In contrast to estimation of the a priori SNR employing the decision-directed (DD) method, which uses the estimated speech spectrum in previous frame, we propose to find the a priori SNR based on the MLR technique by incorporating regression parameters such as the ratio between the local energy of the noisy speech and its derived minimum along with the a posteriori SNR. In the experimental step, regression coefficients obtained using the MLR are assigned according to various noise types, for which we employ a real-time noise classification scheme based on a Gaussian mixture model (GMM). Evaluations using both objective speech quality measures and subjective listening tests under various ambient noise environments show that the performance of the proposed algorithm is better than that of the conventional methods.     

Wavelet Packet Compression of Medical Images

Tolba, A. S., Wavelet Packet Compression of Medical Images, Digital Signal Processing12 (2002) 441–470The increasing need for efficient image storage and transmission in hospitals imposes heavy requirements on the design of picture archiving and communication systems. Thus new methods are needed for efficient image compression. Recent reviews of wavelets in biomedical applications showed that wavelets should be used with caution and that a particular solution should always be motivated by the problem itself. This study discovers the best design parameters for a data compression scheme applied to medical images of different imaging modalities. The proposed technique aims at reducing the transmission cost while preserving the diagnostic integrity. By selecting the wavelet packet's filters, decomposition level and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimum distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of medical images. Statistical significance tests were performed on the experimental measures to conduct the most suitable wavelet shape for each imaging modality. Image quality measures are used to evaluate the performance of different wavelet filters for different imaging modalities. Image resolution is found to have a remarkable effect on the compression rate.     

JPEG2000中不同小波基的图像压缩性能分析

主要讨论了用JPEG2000标准进行静态图像压缩时，小波基的选择对图像压缩性能的影响．我们根据JPEG2000标准，比较了各类图像在不同小波基下的压缩结果，发现在JPEG2000中，小波基的选择在一定程度上影响了图像的压缩性能．对于某些图像，JPEG2000标准的缺省小波基并不能取得较优的压缩结果．文中分析了影响图像压缩性能的小波基与图像的特性，在此基础上，给出JPEG2000进行图像压缩时小波基的选择方法．     

音素关联的多文种语音融合编码方法

依据异类文种之间、同类文种不同语音之间存在音素数据关联的特性,提出多文种语音数据融合编码方法。将不同文种存在的相同音素数据段块按段块模板截取语音样本序列,小波变换,提取特征矢量,生成共享模板集;任意字音或语句音串均按共享模板集提供的元素进行编码与解码;以模板音素串构成的语音记录库按（音节、音素）索引。实验结果表明,单字语音数据压缩比、语音数据存储量、语音还原分段信噪比、主观评价得分等参数均明显优于已有方法,语音还原质量良好。     

Wavelet energy based voice activity detection and adaptive thresholding for efficient speech coding

During the last five decades, extensive researches have been carried out in the field of speech compression, which has resulted in various techniques for speech coding. Researchers have been in full swing for more efficient speech coding and their effort is still continuing in different parts of the world. In this paper we are proposing an alternative method for better speech coding. In the proposed technique we use discrete wavelet transform to decompose the signal and wavelet energy is used to differentiate between active voice region and silence region in the speech signal. Depending upon the region’s status the system, different thresholding strategies have been chosen which leads to a better compression without any loss of speech intelligibility. The proposed method is evaluated in terms of qualitative and quantitative parameters. In this paper we also propose an alternative parameter for MOS values which is here after known as System Recognition Rate.     

Speech Separation Methodology for Hearing Aid

In the design of hearing aids (HA), the real-time speech-enhancement is done. The digital hearing aids should provide high signal-to-noise ratio, gain improvement and should eliminate feedback. In generic hearing aids the performance towards different frequencies varies and non uniform. Existing noise cancellation and speech separation methods drops the voice magnitude under the noise environment. The performance of the HA for frequency response is non uniform. Existing noise suppression methods reduce the required signal strength also. So, the performance of uniform sub band analysis is poor when hearing aid is concern. In this paper, a speech separation method using Non-negative Matrix Factorization (NMF) algorithm is proposed for wavelet decomposition. The Proposed non-uniform filter-bank was validated by parameters like band power, Signal-to-noise ratio (SNR), Mean Square Error (MSE), Signal to Noise and Distortion Ratio (SINAD), Spurious-free dynamic range (SFDR), error and time. The speech recordings before and after separation was evaluated for quality using objective speech quality measures International Telecommunication Union -Telecommunication standard ITU-T P.862.     

A new framework for robust speech recognition in complex channel environments

Channel distortion is one of the major factors which degrade the performances of automatic speech recognition (ASR) systems. Current compensation methods are generally based on the assumption that the channel distortion is a constant or slowly varying bias in an utterance or globally. However, this assumption is not sustained in a more complex circumstance, when the speech records being recognized are from many different unknown channels and have parts of the spectrum completely removed (e.g. band-limited speech). On the one hand, different channels may cause different distortions; on the other, the distortion caused by a given channel varies over the speech frames when parts of the speech spectrum are removed completely. As a result, the performance of the current methods is limited in complex environments. To solve this problem, we propose a unified framework in which the channel distortion is first divided into two subproblems, namely, spectrum missing and magnitude changing. Next, the two types of distortions are compensated with different techniques in two steps. In the first step, the speech bandwidth is detected for each utterance and the acoustic models are synthesized with clean models to compensate for spectrum missing. In the second step, the constant term of the distortion is estimated via the expectation-maximization (EM) algorithm and subtracted from the means of the synthesized model to further compensate for magnitude changing. Several databases are chosen to evaluate the proposed framework. The speech in these databases is recorded in different channels, including various microphones and band-limited channels. Moreover, to simulate more types of spectrum missing, various low-pass and band-pass filters are used to process the speech from the chosen databases. Although these databases and their filtered versions make the channel conditions more challenging for recognition, experimental results show that the proposed framework can substantially improve the performance of ASR systems in complex channel environments.     

Wavelet based speech presence probability estimator for speech enhancement

A reliable speech presence probability (SPP) estimator is important to many frequency domain speech enhancement algorithms. It is known that a good estimate of SPP can be obtained by having a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. Recently, the wavelet denoising with multitaper spectrum (MTS) estimation technique was suggested for such purpose. However, traditional approaches directly make use of the wavelet shrinkage denoiser which has not been fully optimized for denoising the MTS of noisy speech signals. In this paper, we firstly propose a two-stage wavelet denoising algorithm for estimating the speech power spectrum. First, we apply the wavelet transform to the periodogram of a noisy speech signal. Using the resulting wavelet coefficients, an oracle is developed to indicate the approximate locations of the noise floor in the periodogram. Second, we make use of the oracle developed in stage 1 to selectively remove the wavelet coefficients of the noise floor in the log MTS of the noisy speech. The wavelet coefficients that remained are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. To adapt to the enhanced a-posteriori SNR function, we further propose a new method to estimate the generalized likelihood ratio (GLR), which is an essential parameter for SPP estimation. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables an improvement in both the quality and intelligibility of the enhanced speeches.     

Fast Adaptive Wavelet for Remote Sensing Image Compression

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.