Title: Frequency Domain Singular Value Decomposition for Efficient Spatial Audio Coding,
Abstract: Advances in virtual reality have generated substantial interest in accurately
reproducing and storing spatial audio in the higher order ambisonics (HOA)
representation, given its rendering flexibility. Recent standardization for HOA
compression adopted a framework wherein HOA data are decomposed into principal
components that are then encoded by standard audio coding, i.e., frequency
domain quantization and entropy coding to exploit psychoacoustic redundancy. A
noted shortcoming of this approach is the occasional mismatch in principal
components across blocks, and the resulting suboptimal transitions in the data
fed to the audio coder. Instead, we propose a framework where singular value
decomposition (SVD) is performed after transformation to the frequency domain
via the modified discrete cosine transform (MDCT). This framework not only
ensures smooth transition across blocks, but also enables frequency dependent
SVD for better energy compaction. Moreover, we introduce a novel noise
substitution technique to compensate for suppressed ambient energy in discarded
higher order ambisonics channels, which significantly enhances the perceptual
quality of the reconstructed HOA signal. Objective and subjective evaluation
results provide evidence for the effectiveness of the proposed framework in
terms of both higher compression gains and better perceptual quality, compared
to existing methods.