Download A Deep Learning Approach to the Prediction of Time-Frequency Spatial Parameters for Use in Stereo Upmixing This paper presents a deep learning approach to parametric timefrequency parameter prediction for use within stereo upmixing algorithms. The approach presented uses a Multi-Channel U-Net with Residual connections (MuCh-Res-U-Net) trained on a novel dataset of stereo and parametric time-frequency spatial audio data to predict time-frequency spatial parameters from a stereo input signal for positions on a 50-point Lebedev quadrature sampled sphere. An example upmix pipeline is then proposed which utilises the predicted time-frequency spatial parameters to both extract and remap stereo signal components to target spherical harmonic components to facilitate the generation of a full spherical representation of the upmixed sound field.
Download A Comparison of Player Performance in a Gamified Localisation Task Between Spatial Loudspeaker Systems This paper presents an experiment comparing player performance in a gamified localisation task between three loudspeaker configurations: stereo, 7.1 surround-sound and an equidistantly spaced octagonal array. The test was designed as a step towards determining whether spatialised game audio can improve player performance in a video game, thus influencing their overall experience. The game required players to find as many sound sources as possible, by using only sonic cues, in a 3D virtual game environment. Results suggest that the task was significantly easier when listening over a 7.1 surround-sound system, based on feedback from 24 participants. 7.1 was also the most preferred of the three listening conditions. The result was not entirely expected in that the octagonal array did not outperform 7.1. It is thought that, for the given stimuli, this may be a repercussion due to the octagonal array sacrificing an optimal front stereo pair, for more consistent imaging all around the listening space.