MULTI-CHANNEL AUDIO SIGNAL GENERATION

We present a multi-channel audio signal generation scheme based on machine-learning and probabilistic modeling. We start from modeling a multi-channel single-source signal. Such signals are naturally modeled as a single-channel reference signal and a spatial- arrangement (SA) model specified by an SA parameter sequence. We focus on the SA model and assume that the reference signal is de- scribed by some parameter sequence. The SA model parameters are described with a learned probability distribution that is conditioned by the reference-signal parameter sequence and, optionally, an SA conditioning sequence. If present, the SA conditioning sequence specifies a signal class or a specific signal. The single-source method can be used for multi-source signals by applying source separation or by using an SA model that operates on nonoverlapping frequency bands. Our GAN-based stereo coding implementation of the latter approach shows that our paradigm facilitates plausible high-quality rendering at a low bit rate for the SA conditioning.