HOA Sound Object Encoding Using Spherical Harmonic Symmetry
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current spatial audio technologies, such as Higher Order Ambisonics (HOA), face challenges in efficient encoding and decoding processes due to high computational and memory requirements, particularly in virtual reality (VR) and augmented reality (AR) applications, where immersive 360-degree sound experiences are essential but are hindered by the need for flexible loudspeaker setups and precise spatial sound representation.
Innovation Solution
The proposed solution involves encoding sound objects into HOA sound fields using spherical harmonic coefficients, with a lookup table-based approach that pre-computes and stores spherical harmonic values, leveraging symmetry to reduce storage and computational costs, and applying gain corrections to control sound source spread while maintaining energy preservation, allowing for efficient rendering and playback on various loudspeaker configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spherical harmonic coefficients are pre-computed and stored in a lookup table for efficient encoding, then encoding speed and productivity are improved, but memory requirements and device complexity increase
Solution Approach 1:
The spherical harmonic coefficient data is segmented by symmetry properties (e.g., even/odd symmetries in azimuth and elevation angles). Only unique coefficient values for specific angular ranges are stored in the lookup table, while other values are derived through symmetry relationships during encoding, reducing memory footprint while maintaining encoding speed
Solution Approach 2:
Spherical harmonic coefficients are pre-computed and stored in a lookup table before runtime encoding operations. This preliminary action enables fast retrieval during encoding without real-time computation, improving productivity while the table is built offline
2Manufacturing precision
If gain corrections are applied to control sound source spread, then spatial precision and manufacturing precision are improved, but computational complexity increases
Solution Approach 1:
Gain corrections are applied as simple multiplicative factors to spherical harmonic coefficients to control sound source spread. By adjusting the gain parameter, the spatial precision is controlled without requiring complex computational operations, maintaining low computational complexity while achieving precise spatial representation
Data Source
AI summary
A method of encoding sound objects includes receiving a set of monophonic sound inputs. Each of the set of monophonic sound inputs includes position and orientation information of a sound object relative to a source position. The set of monophonic sound inputs are encoded into a higher order ambisonic (HOA) sound field in a spherical harmonics domain based on a spherical harmonics dataset including a subset of all spherical harmonic coefficients for a given subset of azimuth and elevation angles. Some embodiments include decoding the HOA sound field to generate a set of loudspeaker signals.