A comparative evaluation of two acoustic signal dereverberation techniques

Thesis (Elec. E.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, (M.S.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1976.Microfiche copy available in Archives and Engineering.Includes bibliographical references.by James B. Gallemore.M.S

Image processing in the human visual system

Journal ArticleThis work extends the multiplicative visual model to include image texture as suggested by experiments [Campbell, Weisel] linking a low resolution Fourier analysis with neurons in certain parts of the visual cortex. The new model takes image texture into account in the sense that weak texture is accentuated and strong, high contrast texture is attenuated. This model is then used as the basis for an improved image enhancement scheme and an unusually successful method for restoring blurred images. In addition, it is suggested how the model may provide new insights into the problem of finding a quantitatively correct image fidelity criterion. The structure of this model is described in relation to visual neurophysiology and examples are presented of images processed by the new techniques. The research described here also shows how the retinex [Land] can be implemented in a new way which allows the required computations to be carried out on a rectangualr grid

Accurate aeroacoustic measurements in closed-section hard-walled wind tunnels

Noise emissions from aircraft are of major concern to aircraft manufacturers. There are various analytical, empirical and numerical tools to help in the design of quieter aircraft, however aeroacoustic measurements in wind tunnels are still required. There is a growing interest in simultaneous aerodynamic and aeroacoustic measurements in hard-walled closed-section wind tunnels. The research hypothesis of this work is whether accurate aeroacoustic measurements are possible in this type of wind tunnel. Two issues are of particular concern: the reverberation sound field and high background noise levels. De-reverberation, based on an Image Source Model (ISM), is proposed to tackle the first issue by incorporating the reflections in the focused beamformer. This technique is computationally fast and easy to implement. Source Power Integration and deconvolution techniques are shown to be still valid in de-reverberation. Measurements in a closed section wind tunnel have shown that an ISM gives a better estimate of the Green's functions, when compared to free-space Green's functions. Furthermore de-reverberation yielded more accurate source strength estimates from the beamformer. Qualitatively, de-convolved results were no different than when using free-space Green's functions. Simulations have shown that the ISM can become unstable at high frequencies if position errors are present. It is therefore recommended to limit the application of the ISM to frequencies below 10 kHz. At low frequencies the accuracy of beamforming levels is highly dependent on the level of noise contamination of the input data. Removing the diagonal of the cross spectral matrix might not be sufficient to eliminate this noise

A novel framework for high-quality voice source analysis and synthesis

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The analysis, parameterization and modeling of voice source estimates obtained via inverse filtering of recorded speech are some of the most challenging areas of speech processing owing to the fact humans produce a wide range of voice source realizations and that the voice source estimates commonly contain artifacts due to the non-linear time-varying source-filter coupling. Currently, the most widely adopted representation of voice source signal is Liljencrants-Fant's (LF) model which was developed in late 1985. Due to the overly simplistic interpretation of voice source dynamics, LF model can not represent the fine temporal structure of glottal flow derivative realizations nor can it carry the sufficient spectral richness to facilitate a truly natural sounding speech synthesis. In this thesis we have introduced Characteristic Glottal Pulse Waveform Parameterization and Modeling (CGPWPM) which constitutes an entirely novel framework for voice source analysis, parameterization and reconstruction. In comparative evaluation of CGPWPM and LF model we have demonstrated that the proposed method is able to preserve higher levels of speaker dependant information from the voice source estimates and realize a more natural sounding speech synthesis. In general, we have shown that CGPWPM-based speech synthesis rates highly on the scale of absolute perceptual acceptability and that speech signals are faithfully reconstructed on consistent basis, across speakers, gender. We have applied CGPWPM to voice quality profiling and text-independent voice quality conversion method. The proposed voice conversion method is able to achieve the desired perceptual effects and the modified speech remained as natural sounding and intelligible as natural speech. In this thesis, we have also developed an optimal wavelet thresholding strategy for voice source signals which is able to suppress aspiration noise and still retain both the slow and the rapid variations in the voice source estimate

Deconvolution of Quantized-Input Linear Systems: An Information-Theoretic Approach

The deconvolution problem has been drawing the attention of mathematicians, physicists and engineers since the early sixties. Ubiquitous in the applications, it consists in recovering the unknown input of a convolution system from noisy measurements of the output. It is a typical instance of inverse, ill-posed problem: the existence and uniqueness of the solution are not assured and even small perturbations in the data may cause large deviations in the solution. In the last fifty years, a large amount of estimation techniques have been proposed by different research communities to tackle deconvolution, each technique being related to a peculiar engineering application or mathematical set. In many occurrences, the unknown input presents some known features, which can be exploited to develop ad hoc algorithms. For example, prior information about regularity and smoothness of the input function are often considered, as well as the knowledge of a probabilistic distribution on the input source: the estimation techniques arising in different scenarios are strongly diverse. Less effort has been dedicated to the case where the input is known to be affected by discontinuities and switches, which is becoming an important issue in modern technologies. In fact, quantized signals, that is, piecewise constant functions that can assume only a finite number of values, are nowadays widespread in the applications, given the ongoing process of digitization concerning most of information and communication systems. Moreover, hybrid systems are often encountered, which are characterized by the introduction of quantized signals into physical, analog communication channels. Motivated by such consideration, this dissertation is devoted to the study of the deconvolution of continuous systems with quantized input; in particular, our attention will be focused on linear systems. Given the discrete nature of the input, we will show that the whole problem can be interpreted as a paradigmatic digital transmission problem and we will undertake an Information-theoretic approach to tackle it. The aim of this dissertation is to develop suitable deconvolution algorithms for quantized-input linear systems, which will be derived from known decoding procedures, and to test them in different scenarios. Much consideration will be given to the theoretical analysis of these algorithms, whose performance will be rigorously described in mathematical terms

医用超音波における散乱体分布の高解像かつ高感度な画像化に関する研究

Ultrasound imaging as an effective method is widely used in medical diagnosis andNDT (non-destructive testing). In particular, ultrasound imaging plays an important role in medical diagnosis due to its safety, noninvasive, inexpensiveness and real-time compared with other medical imaging techniques. However, in general the ultrasound imaging has more speckles and is low definition than the MRI (magnetic resonance imaging) and X-ray CT (computerized tomography). Therefore, it is important to improve the ultrasound imaging quality. In this study, there are three newproposals. The first is the development of a high sensitivity transducer that utilizes piezoelectric charge directly for FET (field effect transistor) channel control. The second is a proposal of a method for estimating the distribution of small scatterers in living tissue using the empirical Bayes method. The third is a super-resolution imagingmethod of scatterers with strong reflection such as organ boundaries and blood vessel walls. The specific description of each chapter is as follows: Chapter 1: The fundamental characteristics and the main applications of ultrasound are discussed, then the advantages and drawbacks of medical ultrasound are high-lighted. Based on the drawbacks, motivations and objectives of this study are stated. Chapter 2: To overcome disadvantages of medical ultrasound, we advanced our studyin two directions: designing new transducer improves the acquisition modality itself, onthe other hand new signal processing improve the acquired echo data. Therefore, the conventional techniques related to the two directions are reviewed. Chapter 3: For high performance piezoelectric, a structure that enables direct coupling of a PZT (lead zirconate titanate) element to the gate of a MOSFET (metal-oxide semiconductor field-effect transistor) to provide a device called the PZT-FET that acts as an ultrasound receiver was proposed. The experimental analysis of the PZT-FET, in terms of its reception sensitivity, dynamic range and -6 dB reception bandwidth have been investigated. The proposed PZT-FET receiver offers high sensitivity, wide dynamic range performance when compared to the typical ultrasound transducer. Chapter 4: In medical ultrasound imaging, speckle patterns caused by reflection interference from small scatterers in living tissue are often suppressed by various methodologies. However, accurate imaging of small scatterers is important in diagnosis; therefore, we investigated influence of speckle pattern on ultrasound imaging by the empirical Bayesian learning. Since small scatterers are spatially correlated and thereby constitute a microstructure, we assume that scatterers are distributed according to the AR (auto regressive) model with unknown parameters. Under this assumption, the AR parameters are estimated by maximizing the marginal likelihood function, and the scatterers distribution is estimated as a MAP (maximum a posteriori) estimator. The performance of our method is evaluated by simulations and experiments. Through the results, we confirmed that the band limited echo has sufficient information of the AR parameters and the power spectrum of the echoes from the scatterers is properly extrapolated. Chapter 5: The medical ultrasound imaging of strong reflectance scatterers based on the MUSIC algorithm is the main subject of Chapter 5. Previously, we have proposed a super-resolution ultrasound imaging based on multiple TRs (transmissions/receptions) with different carrier frequencies called SCM (super resolution FM-chirp correlation method). In order to reduce the number of required TRs for the SCM, the method has been extended to the SA (synthetic aperture) version called SA-SCM. However, since super-resolution processing is performed for each line data obtained by the RBF (reception beam forming) in the SA-SCM, image discontinuities tend to occur in the lateral direction. Therefore, a new method called SCM-weighted SA is proposed, in this version the SCM is performed on each transducer element, and then the SCM result is used as the weight for RBF. The SCM-weighted SA can generate multiple B-mode images each of which corresponds to each carrier frequency, and the appropriate low frequency images among them have no grating lobes. For a further improvement, instead of simple averaging, the SCM applied to the result of the SCM-weighted SA for all frequencies again, which is called SCM-weighted SA-SCM. We evaluated the effectiveness of all the methods by simulations and experiments. From the results, it can be confirmed that the extension of the SCM framework can help ultrasound imaging reduce grating lobes, perform super-resolution and better SNR(signal-to-noise ratio). Chapter 6: A discussion of the overall content of the thesis as well as suggestions for further development together with the remaining problems are summarized.首都大学東京, 2019-03-25, 博士（工学）首都大学東

A Perceptual Comparison of “Black Box” Modeling Algorithms for Nonlinear Audio Systems

Nonlinear systems identification is a widespread topic of interest, particularly within the audio industry, as these techniques are employed to synthesize black box models of nonlinear audio effects. Given the myriad approaches to black box modeling, questions arise as to whether an “optimal” approach exists, or one that achieves valid subjective results as a model with minimal computational expense. This thesis uses ABX listening tests to compare black box models of three hardware audio effects using two popular nonlinear implementations, along with two proposed modified implementations. Models were constructed in the Hammerstein form using sine sweeps and a novel measurement technique for the filters and nonlinearities, respectively. Testing revolved around null hypotheses assuming no change in model identification regardless of the device modeled, implementation used, or program material of the model stimulus. Results provide clear evidence of an effect on all of these accounts, and support a full rejection of the null hypotheses. Outcomes demonstrate a preferable implementation out of the algorithms tested, and suggest the removal of certain implementations as valid approaches altogether