Spatial Audio Mixing Matrices for Covariance Control
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Solution Overview
Problem
Existing spatial audio processing technologies face challenges in generating optimal audio output signals with defined covariance properties, as they often compromise sound quality by relying on decorrelators and struggle to achieve target covariance matrices, especially in scenarios with limited independent sound components.
Innovation Solution
An apparatus and method that utilize an adaptive mixing solution to generate audio output signals by determining a mixing rule based on first and second covariance properties, allowing for the injection of decorrelated sound energy when necessary, thereby achieving target covariance properties while minimizing the use of decorrelators and preserving sound quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If decorrelators are used to generate audio output signals with target covariance properties, then the desired spatial audio effects can be achieved, but sound quality is compromised
Solution Approach 1:
The patent changes the approach from using decorrelators (which degrade sound quality) to using optimal mixing matrices that transform input channel signals through calculated mixing coefficients. This parameter transformation achieves the target covariance properties while preserving sound quality by working directly with the signal parameters rather than applying post-processing decorrelation effects.
Solution Approach 2:
The patent substitutes the mechanical/decorrelator-based approach with a mathematical mixing matrix approach. Instead of using decorrelators to artificially create spatial effects, the system uses optimal mixing matrices to directly compute the transformation from input to output channels, replacing a quality-compromising mechanism with a mathematically optimal solution.
2Adaptability or versatility
If existing spatial audio processing methods are used, then audio processing can be performed, but the ability to achieve target covariance matrices is limited
Solution Approach 1:
The patent employs an iterative optimization process where the mixing matrix is calculated based on the difference between current and target covariance properties. The system continuously adjusts the mixing coefficients to minimize the error between achieved and desired covariance matrices, providing precise control over the output spatial characteristics.
Solution Approach 2:
The patent makes the mixing matrix dynamic and adaptive rather than static. The mixing coefficients are calculated in real-time based on the input signal characteristics and desired output properties, allowing the system to adapt to different audio content and achieve precise target covariance matrices for various spatial audio scenarios.
3Reliability
If independent sound components are limited, then audio processing becomes more challenging, but existing technologies struggle to maintain sound quality
Solution Approach 1:
The patent transforms the limited independent sound components into the desired spatial distribution through optimal mixing matrices. By changing the parameter relationships between channels through calculated mixing coefficients, the system achieves reliable spatial audio performance even when the input signal has limited independent components, without compromising sound quality.
Data Source
AI summary
An apparatus for generating an audio output signal having two or more audio output channels from an audio input signal having two or more audio input channels includes a provider and a signal processor. The provider is adapted to provide first covariance properties of the audio input signal. The signal processor is adapted to generate the audio output signal by applying a mixing rule on at least two of the two or more audio input channels. The signal processor is configured to determine the mixing rule based on the first covariance properties of the audio input signal and based on second covariance properties of the audio output signal, the second covariance properties being different from the first covariance properties.