Recurrent Timing Neural Networks for Joint F0-Localisation Based Speech Separation
Stuart N. Wrigley, Guy J. Brown · 2007
A novel extension to recurrent timing neural networks (RTNNs) is proposed which allows such networks to exploit a joint interaural time difference-fundamental fre-quency (ITD-F0) auditory cue as opposed to F0 only. This extension involves coupling a second layer of coincidence detectors to a two-dimensional RTNN. The coincidence detectors are tuned to particular ITDs and each feeds excitation to a column in the RTNN. Thus, one axis of the RTNN represents F0 and the other ITD. The resulting behaviour allows sources to be segregated on the basis of their separation in ITD-F0 space. Furthermore, all grouping and segregation activity proceeds within individual frequency channels without recourse to across channel estimates of F0 or ITD that are commonly used in auditory scene analysis ap-proaches. The system has been evaluated using a source separation task operating on spatialised speech signals. 1