Hetero-Associative Memories for Voice Signal and Image Processing
An associative memory AM is a type of neural network commonly used for recalling output patterns from input patterns that might be altered by noise. Most of these models have several constraints that limit their applicability in complex problems. Recently, in  a new AM based on some aspects of human brain was introduced, however the authors only test its accuracy using image patterns. In this paper we show that this model is also robust with other type of patterns such as voice signal patterns. The AM is trained with associations composed by voice signals and their corresponding images. Once trained, when a voice signal is used to stimulate the AM we expect the memory recall the image associated to the voice signal. In order to test the accuracy of the proposal, a benchmark of sounds and images was used.
KeywordsAssociative memories voice signal processing image processing
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