Artificial Intelligent Systems and Machine Learning
Performance of current speech recognition systems severely degrades in the presence of noise and reverberation. While rather simple and effective noise reduction techniques have been extensively applied, coping with reverberation still remains as one of the toughest problems in speech recognition and signal processing. The objective of this paper is to provide improved real- time noise cancelling performance while keeping the high quality of enhanced speech by using new robust adaptive beam former. The beam forming approach is based on a fundamental theory of Normalized Least Mean Squares (NLMS) to improve Signal to Noise Ratio (SNR).The microphone has been implemented with a Voice Activity Detector (VAD) which uses time-delay estimation. To obtain a more robust feature against reverberation, the Linear Predictive (LP) residual calculation is performed. The scope of the journal includes developing technologies in Ant Colony Optimization (ACO),Particle swarm optimization (PSO ),Genetic Algorithms (GAs), Evolunary Algorithms (EAs),Principal Component Analysis (PCA),Iris Recognition Systems, Speech Analysis and Recognition, Automatic Character/Language Recognition, Artificial Intelligence Tools for Computer Vision and Speech (understanding/interpretation),Knowledge Bases and Environments Adaptive Techniques, Artificial Intelligence for Software Engineering, Knowledge-Based System Architectures, Regression Analysis, Decision Trees, Modeling and Simulator Building, Modeling, Estimation and Prediction Techniques, Machine Learning Tools, Intelligent Large Scale Systems, Soft Computing, Linear and Non-Linear Systems, Artificial Perceptual Systems, Recognition Systems of Face and Facial Expressions, Fingerprint Identification and Recognition Systems, Footprint Identification and Recognition Systems, Software Engineering for Artificial intelligence.
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