Neural Comput - Principles and typical computational limitations of sparse speaker separation based on deterministic speech features.

Tópicos

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{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The separation of mixed auditory signals into their sources is an eminent neuroscience and engineering challenge. We reveal the principles underlying a deterministic, neural network-like solution to this problem. This approach is orthogonal to ICA/PCA that views the signal constituents as independent realizations of random processes. We demonstrate exemplarily that in the absence of salient frequency modulations, the decomposition of speech signals into local cosine packets allows for a sparse, noise-robust speaker separation. As the main result, we present analytical limitations inherent in the approach, where we propose strategies of how to deal with this situation. Our results offer new perspectives toward efficient noise cleaning and auditory signal separation and provide a new perspective of how the brain might achieve these tasks.

Resumo Limpo

separ mix auditori signal sourc emin neurosci engin challeng reveal principl under determinist neural networklik solut problem approach orthogon icapca view signal constitu independ realiz random process demonstr exemplarili absenc salient frequenc modul decomposit speech signal local cosin packet allow spars noiserobust speaker separ main result present analyt limit inher approach propos strategi deal situat result offer new perspect toward effici nois clean auditori signal separ provid new perspect brain might achiev task

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