Authors:
Qianglong Zeng
1
and
Ganwen Zeng
2
Affiliations:
1
Bellevue High School, United States
;
2
Data I/O Corporation, United States
Keyword(s):
Bayesian inference, neural network, Adaptive signal processing
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
The primary advantages of high performance associative memory model are its ability to learn fast, store correctly, retrieve information similar to the human “content addressable” memory and it can approximate a wide variety of non-linear functions. Based on a distributed associative neural network, a Bayesian inference probabilistic neural network is designed implementing the learning algorithm and the underlying basic mathematical idea for the adaptive noise cancellation. Simulation results using speech corrupted with low signal to noise ratio in telecommunication environment shows great signal enhancement. A system based on the described method can store words and phrases spoken by the user in a communication channel and subsequently recognize them when they are pronounced as connected words in a noisy environment. The method guarantees system robustness in respect to noise, regardless of its origin and level. New words, pronunciations, and languages can be introduced to the syste
m in an incremental, adaptive mode.
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