Artificial Immune System–Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals

Artificial immune systems (AIS) are intelligent algorithms derived from the principles inspired by the human immune system. In this study, electroencephalography (EEG) signals for four distinct motor movements of human limbs are detected and classified using a negative selection classification algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV–Graz dataset 2a, comprising nine subjects) has been used. Mel frequency cepstral coefficients (MFCCs) are extracted as selected features from recorded EEG signals. Dimensionality reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic algorithm (GA) optimized detectors (artificial lymphocytes) are trained using negative selection algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with the optimized radius of the detector as a nonself detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over nine subjects with a maximum individual subject classification accuracy of 97.5% for subject number eight

Estudo sobre o descasamento de frequência em sistemas de controle ativo de ruído para ruídos de banda estreita

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia ElétricaEsta dissertação discute um problema inerente aos sistemas de controle ativo de ruído (active noise control - ANC) de topologia feedforward que visam atenuar exclusivamente ruídos acústicos de banda estreita (ruídos tonais). Nesse tipo de sistema, sensores de referência não acústicos (tacômetros, sensores ópticos, dentre outros) são utilizados para medir a frequência fundamental do ruído primário. Tal medida é utilizada para sintetizar sinais de referência senoidais que são processados por controladores adaptativos, os quais são responsáveis pela geração de um sinal de antirruído. Esse sinal, através de um transdutor, é inserido no domínio acústico visando se obter o cancelamento do ruído primário. Para essa classe de sistema de ANC é observada uma forte degradação de desempenho quando os sinais de referência são gerados com frequências distintas daquelas que compõem o ruído primário. Tal problema, comumente denominado descasamento de frequência, é o objeto principal dos estudos deste trabalho de pesquisa. Nesse contexto, os principais sistemas de ANC robustos ao problema de descasamento de frequência, encontrados na literatura, são estudados, culminando na proposta de um novo sistema com baixa sensibilidade a tal descasamento. Resultados de simulação atestam que o sistema proposto apresenta muito bom desempenho, especialmente quando o ruído a ser cancelado é não estacionário e o seu componente fundamental é o de maior potência.This dissertation presents an inherent problem of narrowband feedforward active noise control (ANC) systems. In this class of systems, nonacoustic reference sensors (tachometers, optic sensors, among others) are used to measure the fundamental frequency of the primary noise. The obtained measurement is used to synthesize sinusoidal reference signals that are processed by adaptive controllers, which are responsible for generating the antinoise signal. The antinoise is inserted into the acoustic environment by using a transducer, aiming to cancel the primary noise. In this class of ANC systems, a strong performance degradation is observed when the reference signals are synthesized with different frequencies of those that compose the primary noise. This problem, usually called frequency mismatch, is the main topic of study in this research work. In this context, the main ANC systems robust to frequency mismatch from the literature are dicussed as well as a new system exhibiting low sensibility to the frequency mismatch is proposed. Simulation results attest very good performance of the proposed system, particularly in scenarios in which the primary noise is nonstationary and its fundamental component has larger power

ARTICULATORY INFORMATION FOR ROBUST SPEECH RECOGNITION

Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria. Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as `beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as `coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal. Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural speech databases: X-ray microbeam and Aurora-2 were annotated, where the former was used to train a TV-estimator and the latter was used to train a Dynamic Bayesian Network (DBN) based ASR architecture. The DBN architecture used two sets of observation: (a) acoustic features in the form of mel-frequency cepstral coefficients (MFCCs) and (b) TVs (estimated from the acoustic speech signal). In this setup the articulatory gestures were modeled as hidden random variables, hence eliminating the necessity for explicit gesture recognition. Word recognition results using the DBN architecture indicate that articulatory representations not only can help to account for coarticulatory variations but can also significantly improve the noise robustness of ASR system

Robust automatic transcription of lectures

Automatic transcription of lectures is becoming an important task. Possible applications can be found in the fields of automatic translation or summarization, information retrieval, digital libraries, education and communication research. Ideally those systems would operate on distant recordings, freeing the presenter from wearing body-mounted microphones. This task, however, is surpassingly difficult, given that the speech signal is severely degraded by background noise and reverberation

Robust Automatic Transcription of Lectures

Die automatische Transkription von Vorträgen, Vorlesungen und Präsentationen wird immer wichtiger und ermöglicht erst die Anwendungen der automatischen Übersetzung von Sprache, der automatischen Zusammenfassung von Sprache, der gezielten Informationssuche in Audiodaten und somit die leichtere Zugänglichkeit in digitalen Bibliotheken. Im Idealfall arbeitet ein solches System mit einem Mikrofon das den Vortragenden vom Tragen eines Mikrofons befreit was der Fokus dieser Arbeit ist