Acoustic Equalization via Iterative Filter Optimization
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Solution Overview
Problem
Current automated acoustic equalization systems are limited in accurately modifying the acoustic properties of environments, such as vehicle passenger compartments, to match a target sound signature, as they lack precise and optimized filter parameter calculation methods.
Innovation Solution
The method involves measuring impulse responses, calculating frequency responses, establishing an average, interpolating and comparing target sound signatures, and determining filter parameters to reduce differences by processing local maxima and minima, with successive iterations and optimization to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated acoustic equalization systems are used to modify acoustic properties of vehicle passenger compartments, then the ability to match target sound signatures is improved, but the precision and accuracy of filter parameter calculation deteriorates due to lack of optimized methods
Solution Approach 1:
The patent transforms the acoustic equalization problem into the frequency domain by applying Fast Fourier Transform to impulse responses, enabling precise parameter analysis and filter design through frequency response characteristics rather than time-domain signals
Solution Approach 2:
The patent replaces traditional trial-and-adjustment mechanical tuning methods with automated digital signal processing algorithms, using computer-based calculations to determine optimal filter parameters through mathematical optimization rather than manual adjustment
2Manufacturing precision
If multiple microphones and iterative filter optimization are used, then the accuracy of acoustic signature matching is improved, but the complexity of the system increases
Solution Approach 1:
The patent divides the acoustic space into multiple measurement points using several microphones positioned at different locations, allowing comprehensive characterization of the acoustic environment through spatially distributed impulse response measurements that can be processed independently and combined
Solution Approach 2:
The patent implements an iterative optimization process where filter parameters are continuously adjusted based on the calculated difference between actual and target frequency responses, using feedback from performance evaluation to guide parameter refinement until convergence criteria are met
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise and optimized acoustic equalization by iteratively adjusting filter parameters, effectively transforming the vehicle's native acoustic signature to a target signature, enhancing sound quality and matching user-defined curves.
Implementation Method 1
Calculation of the N corresponding frequency responses by Fast Fourier Transform
Implementation Method 2
A second algorithm calculates digital filter coefficients so that, when these filters are applied before sound signals are played through the vehicle's loudspeakers, the vehicle's acoustic signature becomes the target sound signature curve
Data Source
Figure 1
Figure 2~3
AI summary
The present invention relates to a system and to a method for automatic acoustic equalisation, characterised in that it comprises the steps of: measuring N impulse responses R1 1, R1 2, etc., R1 N following the emission of a pre-calibrated sound signal received by N microphones; calculating N corresponding frequency responses by means of fast Fourier transform; establishing an average M of the N frequency responses; translating said average M of the N frequency responses into a logarithmic frequency scale; interpolating a target signature sound curve Cec from a specific number of points selected by a user; translating said target signature sound curve Cec into a logarithmic frequency scale; comparing said averaged response M and said target response Cec by calculating the difference between said averaged response M and said target signature sound response Cec; analysing the curve Cdiff resulting from the difference between said averaged response M and said target response Cec; and determining filter parameters for reducing the difference between said averaged response M and said target response Cec by firstly processing the local maxima in descending order according to the gain thereof, then the local minima, and by carrying out consecutive iterations.