Variable Span Filters for Sound Zone Acoustic Contrast
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Solution Overview
Problem
Existing methods for generating sound zones with a limited number of loudspeakers face challenges in achieving a scalable compromise between sound quality and acoustic contrast, often requiring complex signal processing that compromises either sound quality or acoustic difference.
Innovation Solution
A method involving a processor system that computes spatio-temporal correlation matrices, performs joint eigenvalue decomposition, and generates variable span filters to balance acoustic contrast and errors, allowing users to prioritize a trade-off between these factors, with the option for offline or online computation and dynamic updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Pressure Matching (PM) algorithms are used to minimize acoustic reproduction error, then sound quality is improved, but acoustic contrast between sound zones deteriorates
Solution Approach 1:
The patent transforms the sound zone generation problem into an eigenvalue decomposition problem where the correlation matrix eigenvalues and eigenvectors are used to control acoustic contrast. By changing the parameter representation from direct pressure control to eigenvalue-based control, the system can independently optimize both sound quality (low reproduction error) and acoustic contrast between zones, resolving the contradiction between these two objectives.
2Ease of manufacture
If Acoustic Contrast Control (ACC) algorithms are used to optimize acoustic contrast, then acoustic contrast between sound zones is improved, but signal distortion increases
Solution Approach 1:
The patent uses eigenvalue decomposition to transform the control parameters, allowing independent adjustment of acoustic contrast (via eigenvalue ratios) while maintaining signal fidelity. The eigenvector-based filter design ensures that high acoustic contrast is achieved without introducing significant signal distortion, as the transformation is mathematically orthogonal and preserves signal energy.
3Device complexity
If a limited number of loudspeakers are used, then device complexity is reduced, but the ability to achieve both sound quality and acoustic contrast deteriorates
Solution Approach 1:
The patent changes the control parameters from direct loudspeaker amplitude control to eigenvalue and eigenvector control. This transformation allows the system to achieve optimal performance with fewer loudspeakers by efficiently utilizing the available degrees of freedom. The eigenvalue decomposition provides a compact representation that maximizes the information extracted from limited loudspeaker channels.
Solution Approach 2:
The patent introduces dynamic adaptability through online computation of eigenvalue decomposition and variable span filters. The system can adapt to different room acoustics, loudspeaker configurations, and sound zone requirements in real-time, maintaining high performance with limited hardware resources by dynamically optimizing the control parameters.
4Ease of manufacture
If complex signal processing is used to control loudspeakers, then acoustic contrast between sound zones is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent performs preliminary computation of the correlation matrix and its eigenvalue decomposition offline or in advance. This pre-computation step transforms the complex real-time control problem into a simpler form where only the application of pre-computed eigenvectors and eigenvalues is needed during operation, significantly reducing online computational complexity while maintaining high acoustic contrast performance.
Data Source
AI summary
The invention provides a method for generating output filters to a plurality of loudspeakers at respective positions for playback of a plurality of different input signals in respective spatially different sound zones by means of a processor system. The method comprising computing spatio-temporal correlation matrices in response to spatial information, e.g. measured transfer functions, and in response to desired sound pressures in the plurality of sound zones. Joint eigenvalue decomposition of the spatial correlation matrices are then computed, or at least an approximation thereof, to arrive at eigenvectors accordingly. Next, variable span filters a reformed from a linear combination of the eigenvectors in response to a desired trade-off between acoustic contrast and acoustic errors in the sound zones. Finally, output filter for each of the plurality of loudspeakers, for each of the plurality of input signals, in accordance with the variable span filters. The method is applicable also for optimization in one zone, e.g. for room equalization.