Effects of spatial configuration and fundamental frequency on speech intelligibility in multiple-talker conditions in the ipsilateral horizontal plane and median planea).

Spatial separation and fundamental frequency (F0) separation are effective cues for improving the intelligibility of target speech in multi-talker scenarios. Previous studies predominantly focused on spatial configurations within the frontal hemifield, overlooking the ipsilateral side and the entire median plane, where localization confusion often occurs. This study investigated the impact of spatial and F0 separation on intelligibility under the above-mentioned underexplored spatial configurations. The speech reception thresholds were measured through three experiments for scenarios involving two to four talkers, either in the ipsilateral horizontal plane or in the entire median plane, utilizing monotonized speech with varying F0s as stimuli. The results revealed that spatial separation in symmetrical positions (front-back symmetry in the ipsilateral horizontal plane or front-back, up-down symmetry in the median plane) contributes positively to intelligibility. Both target direction and relative target-masker separation influence the masking release attributed to spatial separation. As the number of talkers exceeds two, the masking release from spatial separation diminishes. Nevertheless, F0 separation remains as a remarkably effective cue and could even facilitate spatial separation in improving intelligibility. Further analysis indicated that current intelligibility models encounter difficulties in accurately predicting intelligibility in scenarios explored in this study.

Multiple Sound Sources Localization with Frame-by-Frame Component Removal of Statistically Dominant Source.

Multiple sound sources localization is a hot topic in audio signal processing and is widely utilized in many application areas. This paper proposed a multiple sound sources localization method based on a statistically dominant source component removal (SDSCR) algorithm by soundfield microphone. The existence of the statistically weak source (SWS) among soundfield microphone signals is validated by statistical analysis. The SDSCR algorithm with joint an intra-frame and inter-frame statistically dominant source (SDS) discriminations is designed to remove the component of SDS while reserve the SWS component. The degradation of localization accuracy caused by the existence of the SWS is resolved using the SDSCR algorithm. The objective evaluation of the proposed method is conducted in simulated and real environments. The results show that the proposed method achieves a better performance compared with the conventional SSZ-based method both in sources localization and counting.