Primitive auditory segregation based on oscillatory correlation

Separation of speech from interfering sounds based on oscillatory correlation

“…One possible realization is to use systematic time delays to maintain a recent history of the auditory input [55], say 150 time frames as used in the speech and telephone mixture. Consistent with the shifting synchronization theory that Wang proposed to explain primitive stream segregation [55], such an architecture implies that oscillator populations corresponding to different streams shift on the oscillator network as the stimuli unfold in time. The second issue is how the past ASA result influences current processing.…”

“…We use the LEGION algorithm described in [55] and [56] for all of our simulations, because integrating a large system of differential equations is very time-consuming. The algorithm follows the major steps in dynamic evolution of the differential equations, and maintains the essential characteristics of the LEGION network, such as two time scales and properties of synchrony and desynchrony.…”

“…In their scheme, a set of auditory elements forms a perceptual stream if the corresponding oscillators are synchronized (phase locked with no phase lag), and are desyn-chronized from oscillators that represent different streams. On the basis of this representation, Wang [53], [55] later proposed a neural architecture for auditory organization (see also Brown and Cooke [9] for a different account also based on oscillations). Wang's architecture is based on new insights into locally excitatory globally inhibitory networks of relaxation oscillators [49], which take into consideration the topological relations between auditory elements.…”

“…Wang's architecture is based on new insights into locally excitatory globally inhibitory networks of relaxation oscillators [49], which take into consideration the topological relations between auditory elements. This oscillatory correlation framework [55] may be regarded as a special form of temporal correlation. Recently, Brown and Wang [10] gave an account of concurrent vowel separation based on oscillatory correlation.…”

“…Generally, these models have attempted to reproduce simple examples of auditory stream segregation using stimuli such as alternating puretone sequences [9], [55]. Even in [10], which models the segregation of concurrent vowel sounds, the neural network operates on a single time frame and is therefore unable to segregate time-varying sounds.…”