Multi-mode filter parameter tuning methods, devices, equipment and storage media

By designing differentiated optimization strategies and intelligent early-stop mechanisms for FF, FB, and EQ filter modes, the problems of low efficiency and unstable accuracy in multi-mode filter parameter tuning are solved, achieving efficient and accurate filter parameter optimization.

CN121908182BActive Publication Date: 2026-06-30HUAQIN TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUAQIN TECH CO LTD
Filing Date
2026-03-24
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing technologies suffer from low efficiency in parameter tuning, unstable accuracy, and low resource utilization in multi-mode filters, especially in large-scale production.

Method used

A differentiated optimization strategy is adopted, with different optimization objectives and initialization strategies designed for three filter modes: FF, FB, and EQ. Combined with the stochastic gradient descent algorithm and intelligent early stopping mechanism, the filter parameters are optimized until the preset convergence conditions are met.

Benefits of technology

It significantly improves the efficiency and accuracy of multi-mode filter parameter tuning, shortens the tuning time, improves product consistency and resource utilization, and reduces resource waste caused by invalid calculations.

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Abstract

This application provides a method, apparatus, device, and storage medium for multi-mode filter parameter tuning, relating to the field of audio signal processing technology. The method includes: acquiring target response data and determining the target filter mode; employing a differentiated optimization strategy corresponding to the target filter mode to iteratively optimize the filter parameter set to be optimized until the corresponding convergence condition is met; the differentiated optimization strategy includes: when the target filter mode is FF mode, the optimization objective is to reduce amplitude and phase errors; when the target filter mode is FB mode, the optimization objective is to improve noise reduction depth and meet preset stability constraints; when the target filter mode is EQ mode, the optimization objective is to reduce amplitude errors; and outputting the optimized filter parameter set as the tuning result. This application significantly improves tuning efficiency and reduces resource consumption while ensuring accuracy by introducing a differentiated optimization strategy and an early stopping mechanism.
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