Biologically inspired speaker verification

Speaker verification is an active research problem that has been addressed using a variety of different classification techniques. However, in general, methods inspired by the human auditory system tend to show better verification performance than other methods. In this thesis three biologically inspired speaker verification algorithms are presented

Ship target recognition

Includes bibliographical references.In this report the classification of ship targets using a low resolution radar system is investigated. The thesis can be divided into two major parts. The first part summarizes research into the applications of neural networks to the low resolution non-cooperative ship target recognition problem. Three very different neural architectures are investigated and compared, namely; the Feedforward Network with Back-propagation, Kohonen's Supervised Learning Vector Quantization Network, and Simpson's Fuzzy Min-Max neural network. In all cases, pre-processing in the form of the Fourier-Modified Discrete Mellin Transform is used as a means of extracting feature vectors which are insensitive to the aspect angle of the radar. Classification tests are based on both simulated and real data. Classification accuracies of up to 93 are reported. The second part is of a purely investigative nature, and summarizes a body of research aimed at exploring new ground. The crux of this work is centered on the proposal to use synthetic range profiling in order to achieve a much higher range resolution (and hence better classification accuracies). Included in this work is a comprehensive investigation into the use of super-resolution and noise reducing eigendecomposition techniques. Algorithms investigated include the Principal Eigenvector Method, the Total Least Squares Method, and the MUSIC method. A final proposal for future research and development concerns the use of time domain averaging to improve the classification performance of the radar system. The use of an iterative correlation algorithm is investigated

Arabic Isolated Word Speaker Dependent Recognition System

In this thesis we designed a new Arabic isolated word speaker dependent recognition system based on a combination of several features extraction and classifications techniques. Where, the system combines the methods outputs using a voting rule. The system is implemented with a graphic user interface under Matlab using G62 Core I3/2.26 Ghz processor laptop. The dataset used in this system include 40 Arabic words recorded in a calm environment with 5 different speakers using laptop microphone. Each speaker will read each word 8 times. 5 of them are used in training and the remaining are used in the test phase. First in the preprocessing step we used an endpoint detection technique based on energy and zero crossing rates to identify the start and the end of each word and remove silences then we used a discrete wavelet transform to remove noise from signal. In order to accelerate the system and reduce the execution time we make the system first to recognize the speaker and load only the reference model of that user. We compared 5 different methods which are pairwise Euclidean distance with MelFrequency cepstral coefficients (MFCC), Dynamic Time Warping (DTW) with Formants features, Gaussian Mixture Model (GMM) with MFCC, MFCC+DTW and Itakura distance with Linear Predictive Coding features (LPC) and we got a recognition rate of 85.23%, 57% , 87%, 90%, 83% respectively. In order to improve the accuracy of the system, we tested several combinations of these 5 methods. We find that the best combination is MFCC | Euclidean + Formant | DTW + MFCC | DTW + LPC | Itakura with an accuracy of 94.39% but with large computation time of 2.9 seconds. In order to reduce the computation time of this hybrid, we compare several subcombination of it and find that the best performance in trade off computation time is by first combining MFCC | Euclidean + LPC | Itakura and only when the two methods do not match the system will add Formant | DTW + MFCC | DTW methods to the combination, where the average computation time is reduced to the half to 1.56 seconds and the system accuracy is improved to 94.56%. Finally, the proposed system is good and competitive compared with other previous researches

Reading the brain’s personality: using machine learning to investigate the relationships between EEG and depressivity

Electroencephalography (EEG) measures electrical signals on the scalp and can give information about processes near the surface of the brain (cortex). The goal of our research was to create models that predict depressivity (mapping to personality in general, not just sickness) and to find potential biomarkers in EEG data. First, to provide our models with cleaner EEG data, we designed a novel single-channel physiology-based eye blink artefact removal method and a mains power noise removal method. Then, we assessed two main machine learning model types (classification- and regression-based) with a total of eighteen sub-types to predict the depressivity of participants. The models were generated by combining four signal processing techniques with a) three classification techniques, and b) three regression techniques. The experimental results showed that both types of models perform well in depressivity prediction and one regression-based model (Reg-FFT-LSBoost) showed a significant depressivity prediction performance, especially for female group. More importantly, we found that a specific EEG frequency band (the gamma band) made major contributions to depressivity prediction. Apart from that, the alpha and beta band may make modest contributions. Specific locations (T7, T8, and C3) made major contributions to depressivity prediction. Frontal locations may also have some influence. We also found that the combination of both eye states’ EEG data showed a better depressivity prediction ability. Compared to the eyes closed data, the EEG data obtained from the state of eyes open were more suitable for assessing depressivity. In brief, the outcomes of this research provided the possibilities for translating the EEG data for depressivity measure. Furthermore, there are possibilities to extend the research to apply to other mental disorders’ prediction, such as anxiety

Fault detection and path optimisation for a meat-processing robot

An automated Y-cutting system has been developed by the Automation Systems team of Industrial Research Limited. This robotic device performs the Y-cut operation on sheep carcasses. The robotic Y-cutting system must deal with a variety of carcass shapes and sizes, and it is important that process faults are detected, diagnosed and corrected as quickly as possible. This thesis addresses the fault detection and path optimisation requirements of the Y-cutting system. The development of a neural network-based fault detection module is documented. This module classifies process faults using axial motor current data from the Y-cutting robot. The module successfully classifies 98% of the presented cut signals during offline training, and 100% of cuts during an extended trial in an Australian meat-processing plant. An online training scheme is implemented to allow for the retraining of the neural network weights as required. The fault detection module is extended to handle a greater number of fault conditions and to detect variations in the process load. A path optimisation algorithm is developed to optimise the parameters that define the cut-path of the robot based on the output of the fault detection module. A line-search within the parameter space is used to estimate the position of the optimum parameter value. The optimisation of fifteen path parameters requires 4760 simulated Y-cuts, equating to approximately 1.5 days of processing in a typical meat-plant. This is significantly faster than the existing method for manually tuning the Y-cutting system. The fault detection and path optimisation systems can be generically applied to other robotic systems produced by Industrial Research Limited for the handling and processing of highly varying natural products

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces