Multi-Channel Compression and Coding of Reverberant Ad-Hoc Recordings Through Spatial Autoregressive Modelling

Autoregressive modelling techniques such as multi-channel linear prediction are widely used for applications such as coding, dereverberation and compression of the speech signals. State of the art multi-channel linear prediction methods do not take into account the locations of the microphones and assume single distance compact microphone arrays. In this paper a spatially modified multichannel autoregressive compression and coding method is proposed and successfully tested in order to adapt the standard multi-channel method to the virtual reality and immersive video conferencing applications where the microphones can be meters away from each other. The proposed method estimates the spatial distances between each microphone and the source to optimise the joint compression of the signals recorded within a wide area. The results suggest that the proposed method outperforms the standard multi-channel compression and coding when applied to the ad-hoc scenarios.