Phoneme Based Speaker Verification System Based on Two Stage Self-Organizing Map Design

Speaker verification is one of the pattern recognition task that authenticate a person by his or her voice. This thesis deals with a relatively new technique of classification that is the self-organizing map (SOM). Self-organizing map, as an unsupervised learning artificial neural network, rarely used as final classification step in pattern recognition task due to its relatively low accuracy. A two-stage self-organizing map design has been implemented in this thesis and showed improved results over conventional single stage design. For speech features extraction, this thesis does not introduce any new technique. A well study method that is the linear prediction analysis (LP A) has been used. Linear predictive analysis derived coefficients are extracted from segmented raw speech signal to train and test the front stage self-organizing map. Unlike other multistage or hierarchical self-organizing map designs, this thesis utilized residual vectors generated from front stage self-organizing map to train and test the second stage selforganizing map. The results showed that by breaking the classification tasks into two level or more detail resolution, an improvement of more than 5% can be obtained. Moreover, the computation time is also reduced greatly

Balancing Guidance Range and Strength Optimizes Self-Organization by Silicon Growth Cones

We characterize the first hardware implementation of a self-organizing map algorithm based on axon migration. A population of silicon growth cones automatically wires a topographic mapping by migrating toward sources of a diffusible guidance signal that is released by postsynaptic activity. We varied the diffusion radius of this signal, trading strength for range. Best performance is achieved by balancing signal strength against signal range

Classification of EMG Signal Based on Human Percentile using SOM

Electromyography (EMG) is a bio signal that is formed by physiological variations in the state of muscle fibre membranes. Pattern recognition is one of the fields in the bio-signal processing which classified the signal into certain desired categories with subject to their area of application. This study described the classification of the EMG signal based on human body percentile using Self Organizing Mapping (SOM) technique. Different human percentile definitively varies the arm circumference size. Variation of arm circumference is due to fatty tissue that lay between active muscle and skin. Generally the fatty tissue would decrease the overall amplitude of the EMG signal. Data collection is conducted randomly with fifteen subjects that have numerous percentiles using non-invasive technique at Biceps Brachii muscle. The signals are then going through filtering process to prepare them for the next stage. Then, five well known time domain feature extraction methods are applied to the signal before the classification process. Self Organizing Map (SOM) technique is used as a classifier to discriminate between the human percentiles. Result shows that SOM is capable in clustering the EMG signal to the desired human percentile categories by optimizing the neurons of the technique

Automatic condition monitoring of electromechanical system based on MCSA, spectral kurtosis and SOM neural network

Condition monitoring and fault diagnosis play the most important role in industrial applications. The gearbox system is an essential component of mechanical system in fault identification and classification domains. In this paper, we propose a new technique which is based on the Fast-Kurtogram method and Self Organizing Map (SOM) neural network to automatically diagnose two localized gear tooth faults: a pitting and a crack. These faults could have very different diagnostics; however, the existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. With the aim to automatically diagnose these two faults, a dynamic model of an electromechanical system which is a simple stage gearbox with and without defect driven by a three phase induction machine is proposed, which makes it possible to simulate the effect of pitting and crack faults on the induction stator current signal. The simulated motor current signal is then analyzed by using a Fast-Kurtogram method. Self-organizing map (SOM) neural network is subsequently used to develop an automatic diagnostic system. This method is suitable for differentiating between a pitting and a crack fault

Significant variables extraction of post-stroke EEG signal using wavelet and SOM kohonen

Stroke patients require a long recovery. One success of the treatment given is the evaluation and monitoring during recovery. One device for monitoring the development of post-stroke patients is Electroencephalogram (EEG). This research proposed a method for extracting variables of EEG signals for post-stroke patient analysis using Wavelet and Self-Organizing Map Kohonen clustering. EEG signal was extracted by Wavelet to obtain Alpha, beta, theta, gamma, and Mu waves. These waves, the amplitude and asymmetric of the symmetric channel pairs are features in Self Organizing Map Kohonen Clustering. Clustering results were compared with actual clusters of post-stroke and no-stroke subjects to extract significant variable. These results showed that the configuration of Alpha, Beta, and Mu waves, amplitude together with the difference between the variable of symmetric channel pairs are significant in the analysis of post-stroke patients. The results gave using symmetric channel pairs provided 54-74% accuracy

Biometric signals compression with time- and subject-adaptive dictionary for wearable devices

This thesis work is dedicated to the design of a lightweight compression technique for the real-time processing of biomedical signals in wearable devices. The proposed approach exploits the unsupervised learning algorithm of the time-adaptive self-organizing map (TASOM) to create a subject-adaptive codebook applied to the vector quantization of a signal. The codebook is obtained and then dynamically refined in an online fashion, without requiring any prior information on the signal itsel

ANALYSIS AND SYNTHESIS OF SIMPLE DIGITAL SPIKE MAPS

This paper considers a digital spike map and the learning algorithm. A digital spike map is a simple digital dynamical system defined on a set point. Depending on parameters and initial condition, the digital spike map generates a periodic orbit in the steady state. In order to analyze the phenomenon by Dmap, we introduce simple feature quantities. There are various applications examples of Dmap research, but here we consider dmap obtained by quantizing Amap. And, we consider Dmap self-organizing learning. In order to construct a desired digital spike map, we introduce a simple learning method based on the self-organizing feature map. As a chaotic teacher signal is applied, the learning method updates the closest element of the map and its neighbors successively. After the learning, the digital spike map can have various kinds of periodic orbits. This learning algorithm is simple and self-organizing. The dynamics of the digital spike map is investigated by simple feature quantities and typical results are demonstrated. We consider the learning process and results from basic numerical experiments

Development of self-organizing methods for radio spectrum sensing

A problem of wide-band radio spectrum analysis in real time was solved and presented in the dissertation. The goal of the work was to develop a spectrum sensing method for primary user emission detection in radio spectrum by investigating new signal feature extraction and intelligent decision making techniques. A solution of this problem is important for application in cognitive radio systems, where radio spectrum is analyzed in real time. In thesis there are reviewed currently suggested spectrum analysis methods, which are used for cognitive radio. The main purpose of these methods is to optimize spectrum description feature estimation in real-time systems and to select suitable classification threshold. For signal spectrum description analyzed methods used signal energy estimation, analyzed energy statistical difference in time and frequency. In addition, the review has shown that the wavelet transform can be used for signal pre-processing in spectrum sensors. For classification threshold selection in literature most common methods are based on statistical noise estimate and energy statistical change analysis. However, there are no suggested efficient methods, which let classification threshold to change adaptively, when RF environment changes. It were suggested signal features estimation modifications, which let to increase the efficiency of algorithm implementation in embedded system, by decreasing the amount of required calculations and preserving the accuracy of spectrum analysis algorithms. For primary signal processing it is suggested to use wavelet transform based features extraction, which are used for spectrum sensors and lets to increase accuracy of noisy signal detection. All primary user signal emissions were detected with lower than 1% false alarm ratio. In dissertation, there are suggested artificial neural network based methods, which let adaptively select classification threshold for the spectrum sensors. During experimental tests, there was achieved full signals emissions detection with false alarm ratio lower than 1%. It was suggested self organizing map structure modification, which increases network self-training speed up to 32 times. This self-training speed is achieved due to additional inner weights, which are added in to self organizing map structure. In self-training stage network structure changes especially fast and when topology, which is suited for given task, is reached, in further self-training iterations it can be disordered. In order to avoid this over-training, self-training process monitoring algorithms must be used. There were suggested original methods for self-training process control, which let to avoid network over-training and decrease self-training iteration quantity

Signal processing and health assessment techniques in structural health monitoring

Considering the practical necessity of realizing the aircraft structure health monitoring (SHM), not only some advanced sensors must be adopted, an appropriate signal processing method is also necessary, in order to achieve the goals of signal characteristic extraction and health assessment. Based on the fact that piezoelectric (PZT) sensor signals extracted from the monitored structure carry a lot of useful health related information, this paper proposes to utilize the power characteristics of PZT signals for health assessment of the aircraft structure. The Hilbert-Huang Transform (HHT) algorithm is employed to calculate the power characteristic vector, after that, the self-organizing map (SOM) is used to map the corresponding characteristics into a confidence value (CV) which represents the health state of the monitored structure. The experimental results demonstrate that the proposed method is reliable