Speech localisation in a multitalker mixture by humans and machines

Speech localisation in multitalker mixtures is affected by the listener’s expectations about the spatial arrangement of the sound sources. This effect was investigated via experiments with human listeners and a machine system, in which the task was to localise a female-voice target among four spatially distributed male-voice maskers. Two configurations were used: either the masker locations were fixed or the locations varied from trial-to-trial. The machine system uses deep neural networks (DNNs) to learn the relationship between binaural cues and source azimuth, and exploits top-down knowledge about the spectral characteristics of the target source. Performance was examined in both anechoic and reverberant conditions. Our experiments show that the machine system outperformed listeners in some conditions. Both the machine and listeners were able to make use of a priori knowledge about the spatial configuration of the sources, but the effect for headphone listening was smaller than that previously reported for listening in a real room

Local sound field synthesis

This thesis investigates the physical and perceptual properties of selected methods for (Local) Sound Field Synthesis ((L)SFS). In agreement with numerical sound field simulations, a specifically developed geometric model shows an increase of synthesis accuracy for LSFS compared to conventional SFS approaches. Different (L)SFS approaches are assessed within listening experiments, where LSFS performs at least as good as conventional methods for azimuthal sound source localisation and achieves a significant increase of timbral fidelity for distinct parametrisations.Die Arbeit untersucht die physikalischen und perzeptiven Eigenschaften von ausgewählten Verfahren zur (lokalen) Schallfeldsynthese ((L)SFS). Zusammen mit numerischen Simulationen zeigt ein eigens entwickeltes geometrisches Modell, dass LSFS gegenüber konventioneller SFS zu einer genauere Synthese führt. Die Verfahren werden in Hörversuchen evaluiert, wobei LSFS bei der horizontalen Lokalisierung von Schallquellen eine Genauigkeit erreicht, welche mindestens gleich der von konventionellen Methoden ist. Für bestimmte Parametrierung wird eine signifikant verbesserte klangliche Treue erreicht