Perceptual Audio Object Coding Using Adaptive Subband Grouping with CNN and Residual Block

Spatial audio content is becoming increasingly popular and is regarded as a set of object signals with associated metadata. The object-based content representation is independent of loudspeaker layouts and provides high spatial resolution when reproduced on more loudspeakers. The audio quality of the traditional spatial audio object coding (SAOC) method has severe aliasing distortion, which impairs the immersive listening experience. In this study, we reduce aliasing distortion by perceptual adaptive subband grouping strategy and use the convolutional neural network (CNN) and residual block to build the side information compressing model. Both objective and subjective experiments on benchmark datasets with different bitrates show that the proposed method achieves favorable performance against state-of-the-art methods.