Evaluation of Noise Reduction Methods for Sentence Recognition by Sinhala Speaking Listeners

Noise reduction is a crucial aspect of hearing aids, which researchers have been striving to address over the years. However, most existing noise reduction algorithms have primarily been evaluated using English. Considering the linguistic differences between English and Sinhala languages, including variation in syllable structures and vowel duration, it is very important to assess the performance of noise reduction tailored to the Sinhala language. This paper presents a comprehensive analysis between wavelet transformation and adaptive filters for noise reduction in Sinhala languages. We investigate the performance of ten wavelet families with soft and hard thresholding methods against adaptive filters with Normalized Least Mean Square, Least Mean Square Average Normalized Least Mean Square, Recursive Least Square, and Adaptive Filtering Averaging optimization algorithms along with cepstral and energy-based voice activity detection algorithms. The performance evaluation is done using objective metrics; Signal to Noise Ratio (SNR) and Perceptual Evaluation of Speech Quality (PESQ) and a subjective metric; Mean Opinion Score (MOS). A newly recorded Sinhala language audio dataset and the NOIZEUS database by the University of Texas, Dallas were used for the evaluation. Our code is available at https://github.com/ChathukiKet/Evaluation-of-Noise-Reduction-Method

Techniques for enhancing digital images

The images obtain from either research studies or optical instruments are often corrupted with noise. Image denoising involves the manipulation of image data to produce a visually high quality image. This thesis reviews the existing denoising algorithms and the filtering approaches available for enhancing images and/or data transmission. Spatial-domain and Transform-domain digital image filtering algorithms have been used in the past to suppress different noise models. The different noise models can be either additive or multiplicative. Selection of the denoising algorithm is application dependent. It is necessary to have knowledge about the noise present in the image so as to select the appropriated denoising algorithm. Noise models may include Gaussian noise, Salt and Pepper noise, Speckle noise and Brownian noise. The Wavelet Transform is similar to the Fourier transform with a completely different merit function. The main difference between Wavelet transform and Fourier transform is that, in the Wavelet Transform, Wavelets are localized in both time and frequency. In the standard Fourier Transform, Wavelets are only localized in frequency. Wavelet analysis consists of breaking up the signal into shifted and scales versions of the original (or mother) Wavelet. The Wiener Filter (mean squared estimation error) finds implementations as a LMS filter (least mean squares), RLS filter (recursive least squares), or Kalman filter. Quantitative measure (metrics) of the comparison of the denoising algorithms is provided by calculating the Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE) value and the Mean Absolute Error (MAE) evaluation factors. A combination of metrics including the PSNR, MSE, and MAE are often required to clearly assess the model performance

Offline and real time noise reduction in speech signals using the discrete wavelet packet decomposition

This thesis describes the development of an offline and real time wavelet based speech enhancement system to process speech corrupted with various amounts of white Gaussian noise and other different noise types

Development of a sub-miniature acoustic sensor for wireless monitoring of heart rate

This thesis presents the development of a non-invasive, wireless, low-power, phonocardiographic (PCG) or heart sound sensor platform suitable for long-term monitoring of heart function. The core of this development process involves a study of the feasibility of this conceptual system and the development of a prototype mixed-signals integrated circuit (IC) to form the integral component of the proposed sensor. The feasibility study of the proposed long-term monitoring sensor is divided into two main parts. The first part of the study investigates the technological aspect of the conceptual system, via a system level design. This is to prove the technological or operational feasibility of the system, where the system can be built completely using discrete, off-the-shelf electronics components to satisfy the size, power consumption, battery life and operational requirements of the sensor platform. The second part of the study concentrates on the post-processing of the heart sounds and murmurs or PCG data recorded. This is where a number of different de-noising algorithms are studied and their relative performance compared when applied to a variety of different noisy heart sound signals that would likely be acquired using the proposed sensor in everyday life. This was done to demonstrate the functional feasibility of the proposed system, where the ambient acoustic noise in the recorded PCG data can be effectively suppressed and therefore meaningful analysis of heart function i.e. heart rate, can be performed on the data. After the feasibility of the conceptual system has been demonstrated, the final part of this thesis discusses the synthesis and testing of a 0.35 μm CMOS technology prototype mixed analog-digital integrated circuit (IC) to miniaturise part of this sensor platform outlined in the system level design, conducted in the earlier part of this thesis, to achieve the objective specifications – in terms of the size and power consumption. A new implementation of the multi-tanh triplet transconductor is introduced to construct a pair of 100 nW analogue 4th order Gm-C signal conditioning filters. Furthermore, a 7 μW digital circuit was designed to drive the analog-to-digital conversion cycle of the Linear Technology LTC1288 ADC and synchronise the ADC’s output to generate the Manchester encoded data compatible with the Holt Integrated Circuit HI-15530 Manchester Encoder/Decoder

Removal of artifacts from electrocardiogram

The electrocardiogram is the recording of the electrical potential of heart versus time. The analysis of ECG signal has great importance in the detection of cardiac abnormalities. The electrocardiographic signals are often contaminated by noise from diverse sources. Noises that commonly disturb the basic electrocardiogram are power line interference, instrumentation noise, external electromagnetic field interference, noise due to random body movements and respirational movements. These noises can be classified according to their frequency content. It is essential to reduce these disturbances in ECG signal to improve accuracy and reliability. Different types of adaptive and non-adaptive digital filters have been proposed to remove these noises. In this thesis, window based FIR filters, adaptive filters and wavelet filter bank are applied to remove the noises. Performances of the filters are compared based on the PSNR values. It is difficult to apply filters with fixed filter coefficients to reduce the instrumentation noise, because the time varying behaviour of this noise is not exactly known. Adaptive filter technique is required to overcome this problem, as the filter coefficients can be varied to track the dynamic variations of the signals. In wavelet transform, a signal is analyzed and expressed as a linear combination of the summation of the product of the wavelet coefficients and mother wavelet. The wavelet decomposition offers an excellent resolution both in time and frequency domain. Better estimation of the amplitudes is also obtained in wavelet based denoising

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems