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- aggregation classification "P1".
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- aggregation creator person.
- aggregation date "2012".
- aggregation format "application/pdf".
- aggregation hasFormat 3163991.bibtex.
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- aggregation isPartOf urn:isbn:9781467314909.
- aggregation isPartOf urn:issn:1098-7576.
- aggregation language "eng".
- aggregation publisher "IEEE".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Attention-driven auditory stream segregation using a SOM coupled with an excitatory-inhibitory ANN".
- aggregation abstract "Auditory attention is an essential property of human hearing. It is responsible for the selection of information to be sent to working memory and as such to be perceived consciously, from the abundance of auditory information that is continuously entering the ears. Thus, auditory attention heavily influences human auditory perception and systems simulating human auditory scene analysis would benefit from an attention model. In this paper, a human-mimicking model of auditory attention is presented, aimed to be used in environmental sound monitoring. It relies on a Self-Organizing Map (SOM) for learning and classifying sounds. Coupled to this SOM, an excitatory-inhibitory artificial neural network (ANN), simulating the auditory cortex, is defined. The activation of these neurons is calculated based on an interplay of various excitatory and inhibitory inputs. The latter simulate auditory attention mechanisms in a human-inspired but simplified way, in order to keep the computational cost within bounds. The behavior of the model incorporating all of these mechanisms is investigated, and plausible results are obtained.".
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- aggregation endPage "523".
- aggregation startPage "516".
- aggregation aggregates 3164001.
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