# Frequency Domain Singular Value Decomposition for Efficient Spatial Audio Coding

## TL;DR

This paper introduces a frequency domain SVD-based framework for spatial audio coding that improves transition smoothness, energy compaction, and perceptual quality in HOA compression, outperforming existing methods.

## Contribution

It proposes a novel frequency domain SVD approach with frequency-dependent energy compaction and a noise substitution technique for enhanced HOA audio coding.

## Key findings

- Higher compression gains demonstrated
- Improved perceptual quality confirmed by evaluations
- Smoother transitions across audio blocks achieved

## 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.