A study on different linear and non-linear filtering techniques of speech and speech recognition

In any signal noise is an undesired quantity, however most of thetime every signal get mixed with noise at different levels of theirprocessing and application, due to which the information containedby the signal gets distorted and makes the whole signal redundant.A speech signal is very prominent with acoustical noises like bubblenoise, car noise, street noise etc. So for removing the noises researchershave developed various techniques which are called filtering. Basicallyall the filtering techniques are not suitable for every application,hence based on the type of application some techniques are betterthan the others. Broadly, the filtering techniques can be classifiedinto two categories i.e. linear filtering and non-linear filtering.In this paper a study is presented on some of the filtering techniqueswhich are based on linear and nonlinear approaches. These techniquesincludes different adaptive filtering based on algorithm like LMS,NLMS and RLS etc., Kalman filter, ARMA and NARMA time series applicationfor filtering, neural networks combine with fuzzy i.e. ANFIS. Thispaper also includes the application of various features i.e. MFCC,LPC, PLP and gamma for filtering and recognition

A wide area system for power transmission security enhancement using a process systems approach

Imperial Users onl

Local polynomial modeling and variable bandwidth selection for time-varying linear systems

This paper proposes a local polynomial modeling (LPM) approach and variable bandwidth selection (VBS) algorithm for identifying time-varying linear systems (TVLSs). The proposed method models the time-varying coefficients of a TVLS locally by polynomials, which can be estimated by least squares estimation with a kernel having a certain bandwidth. The asymptotic behavior of the proposed LPM estimator is studied, and the existence of an optimal local bandwidth which minimizes the local mean-square error is established. A new data-driven VBS algorithm is then proposed to estimate this optimal variable bandwidth adaptively and locally. An individual bandwidth is assigned for each coefficient instead of the whole coefficient vector so as to improve the accuracy in fast-varying systems encountered in fault detection and other applications. Important practical issues such as online implementation are also discussed. Simulation results show that the LPM-VBS method outperforms conventional TVLS identification methods, such as the recursive least squares algorithm and generalized random walk Kalman filter/smoother, in a wide variety of testing conditions, in particular, at moderate to high signal-to-noise ratio. Using local linearization, the LPM method is further extended to identify time-varying systems with mild nonlinearities. Simulation results show that the proposed LPM-VBS method can achieve a satisfactory performance for mildly nonlinear systems based on appropriate linearization. Finally, the proposed method is applied to a practical problem of voltage-flicker-tracking problem in power systems. The usefulness of the proposed approach is demonstrated by its improved performance over other conventional methods. © 2006 IEEE.published_or_final_versio

Development of a Post-Processing Algorithm for Accurate Human Skull Profile Extraction via Ultrasonic Phased Arrays

Ultrasound Imaging has been favored by clinicians for its safety, affordability, accessibility, and speed compared to other imaging modalities. However, the trade-offs to these benefits are a relatively lower image quality and interpretability, which can be addressed by, for example, post-processing methods. One particularly difficult imaging case is associated with the presence of a barrier, such as a human skull, with significantly different acoustical properties than the brain tissue as the target medium. Some methods were proposed in the literature to account for this structure if the skull\u27s geometry is known. Measuring the skull\u27s geometry is therefore an important task that requires attention. In this work, a new edge detection method for accurate human skull profile extraction via post-processing of ultrasonic A-Scans is introduced. This method, referred to as the Selective Echo Extraction algorithm, SEE, processes each A-Scan separately and determines the outermost and innermost boundaries of the skull by means of adaptive filtering. The method can also be used to determine the average attenuation coefficient of the skull. When applied to simulated B-Mode images of the skull profile, promising results were obtained. The profiles obtained from the proposed process in simulations were found to be within 0.15 λ ± 0.11 λ or 0.09 ± 0.07 mm from the actual profiles. Experiments were also performed to test SEE on skull mimicking phantoms with major acoustical properties similar to those of the actual human skull. With experimental data, the profiles obtained with the proposed process were within 0.32 λ ± 0.25 λ or 0.19 ± 0.15 mm from the actual profile

Structured least squares problems and robust estimators

Cataloged from PDF version of article.A novel approach is proposed to provide robust and accurate estimates for linear regression problems when both the measurement vector and the coefficient matrix are structured and subject to errors or uncertainty. A new analytic formulation is developed in terms of the gradient flow of the residual norm to analyze and provide estimates to the regression. The presented analysis enables us to establish theoretical performance guarantees to compare with existing methods and also offers a criterion to choose the regularization parameter autonomously. Theoretical results and simulations in applications such as blind identification, multiple frequency estimation and deconvolution show that the proposed technique outperforms alternative methods in mean-squared error for a significant range of signal-to-noise ratio values

A novel approach to damage localisation based on bispectral analysis and neural network

The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies