Bayesian Optimization for Simultaneous Loudspeaker Deconvolution
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Solution Overview
Problem
Conventional loudspeaker-room equalization methods require significant time and resources due to the sequential measurement and deconvolution of impulse responses from multiple loudspeakers, which increases measurement time and reduces efficiency.
Innovation Solution
The implementation of Bayesian optimization for simultaneous deconvolution of loudspeaker-room impulse responses, where machine learning is applied to optimize stimuli parameters to achieve the shortest possible duration for simultaneous deconvolution of multiple impulse responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequential measurement and deconvolution of impulse responses from multiple loudspeakers is performed, then measurement precision is maintained, but measurement time increases significantly
Solution Approach 1:
The patent combines multiple sequential measurement processes into a single simultaneous measurement process. By exciting multiple loudspeakers at the same time with specially designed stimuli signals and performing joint deconvolution, the system measures impulse responses from all loudspeakers in parallel rather than sequentially, thereby reducing total measurement time while maintaining accuracy through the optimized measurement approach
Solution Approach 2:
The patent employs periodic stimuli signals (such as swept sine signals or maximum length sequences) to excite the loudspeakers. These periodic signals allow for accurate impulse response extraction through correlation or deconvolution techniques, enabling precise measurements even when multiple loudspeakers are excited simultaneously, thus maintaining measurement precision while reducing time
2Measurement precision
If sequential deconvolution of multiple impulse responses is performed, then deconvolution accuracy is maintained, but processing time increases
Solution Approach 1:
The patent merges multiple separate deconvolution operations into a single simultaneous deconvolution process. By formulating the deconvolution problem for all loudspeakers as a unified system and solving it in parallel using optimized algorithms, the system achieves the same deconvolution accuracy as sequential processing but with significantly improved processing efficiency and reduced computational time
3Loss of time
If multiple loudspeakers are excited simultaneously with optimized stimuli, then measurement time is reduced, but stimuli parameter optimization complexity increases
Solution Approach 1:
The patent applies parameter optimization techniques to design stimuli signals with specific characteristics that enable successful simultaneous deconvolution. By optimizing parameters such as signal amplitude, frequency content, time duration, and phase relationships in the stimuli signals, the system achieves accurate impulse response measurement from multiple simultaneously excited loudspeakers, reducing measurement time while managing complexity through systematic parameter optimization
Data Source
AI summary
One embodiment provides a method comprising optimizing one or more stimuli parameters by applying machine learning to training data. The method further comprises determining, based on the one or more optimized stimuli parameters, stimuli for simultaneously exciting a plurality of speakers within a spatial area. The stimuli has a shortest possible duration that is accurate for simultaneous deconvolution of a plurality of impulse responses of the plurality of speakers. The method further comprises simultaneously exciting the plurality of speakers by providing the stimuli to the plurality of speakers at the same time for reproduction. The method further comprises simultaneously deconvolving the plurality of impulse responses based on the stimuli and one or more measurements of sound recorded during the reproduction and arriving at one or more microphones within the spatial area.