Audio Parameter Interpolation for Spatial Audio Coding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audio source coding systems face challenges in achieving high-quality spatial audio reconstruction while minimizing data transmission, particularly in optimizing the trade-off between time and frequency resolution of spatial parameters and efficiently signaling these parameters.
Innovation Solution
The solution involves a decoder and encoder system that uses adaptive interpolation characteristics to generate high-resolution parametric information from low-resolution data, with features like estimation and signaling of interpolation curves, implicit interpolation rules, and recalculating parameters to the upmix matrix domain for efficient interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If low-resolution parametric information is transmitted to reduce data overhead, then data transmission efficiency is improved, but spatial audio reconstruction quality deteriorates
Solution Approach 1:
The encoder pre-calculates and transmits auxiliary information (interpolation curves, characteristic values, or high-resolution parameter sets) that enables the decoder to reconstruct high-resolution parametric information from low-resolution transmitted data. This preliminary preparation of reconstruction data allows quality recovery without increasing transmitted data volume.
Solution Approach 2:
Interpolation curves and characteristic values act as intermediaries between the transmitted low-resolution parameters and the reconstructed high-resolution parameters. These intermediary elements carry the essential information needed to bridge the resolution gap, enabling accurate spatial audio reconstruction from compressed data.
2Manufacturing precision
If high-resolution parametric information is transmitted to improve spatial audio reconstruction quality, then spatial audio reconstruction quality is improved, but data overhead increases
Solution Approach 1:
The system extracts only the most essential and informative parameters for transmission at low resolution, while deriving the remaining high-resolution parameters through interpolation and reconstruction algorithms. This selective extraction minimizes transmitted data while preserving reconstruction quality.
Solution Approach 2:
The system changes the resolution parameter of transmitted data to low-resolution, while using interpolation curves and characteristic values to restore high-resolution parameters at the decoder. This parameter transformation allows quality preservation without proportionate increase in data transmission.
3Measurement precision
If frequent parameter updates are performed to improve time resolution, then time resolution is improved, but coding efficiency deteriorates
Solution Approach 1:
The system updates parameters periodically at lower frequencies, using interpolation curves to generate high-resolution parameter values between update points. This periodic updating with interpolation maintains perceived time resolution while reducing actual parameter transmission frequency, thereby improving coding efficiency.
Solution Approach 2:
Interpolation curves are pre-calculated and stored, allowing the system to generate high-resolution parameter values without real-time computation. This preliminary preparation enables efficient decoding with high time resolution without requiring frequent parameter transmissions.
Data Source
AI summary
A parameter calculator calculates lower resolution parametric information and interpolation information. On a decoder-side, an upmixer is used for generating the output channels. The upmixer uses high resolution parametric information generated by a parameter interpolator using the low resolution parametric information and decoder-side derived interpolation information or encoder-generated interpolation information for selecting one of a plurality of different interpolation characteristics.