Equalizer Curve Selection for Faster User Preference Matching
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Solution Overview
Problem
Users find it difficult to accurately and quickly set audio equalizer parameters to match their personal preferences, as existing technologies either have significant limitations or are overly complex.
Innovation Solution
A method that iteratively selects and updates equalizer curves based on user preference data, using a group evolutionary strategy to reduce the search space and improve convergence speed, allowing for the accurate setting of equalizer parameters through multiple iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed frequency band gain mode audio equalizer is used, then the device complexity is reduced, but the adaptability to user preferences deteriorates
Solution Approach 1:
The system automatically performs iterative selection and evaluation of equalizer curves based on user feedback, eliminating the need for manual adjustment by users. The audio device itself conducts the optimization process by presenting curve pairs, receiving user preferences, and automatically converging to the optimal equalizer settings through multiple iterations.
2Adaptability or versatility
If a customizable frequency band gain mode audio equalizer is used, then the adaptability to user preferences is improved, but the device complexity increases
Solution Approach 1:
The system introduces an intermediary automated selection process that mediates between the user's simple preference indication and the complex equalizer parameter space. Instead of directly exposing users to complex frequency band adjustments, the system acts as an intermediary that translates simple user feedback into precise equalizer curve selections through iterative optimization.
Solution Approach 2:
The equalizer adjustment process is segmented into multiple iterative steps, where each step presents only two candidate curves to the user for selection. This segmentation breaks down the complex task of adjusting multiple frequency bands into a series of simple binary choices, reducing the cognitive load on users while maintaining precision in the final result.
3Manufacturing precision
If iterative selection with group evolutionary strategy is used, then the manufacturing precision of equalizer parameters is improved, but the loss of time increases
Solution Approach 1:
The system employs periodic iterative action where equalizer curves are selected and evaluated in repeated cycles. Each iteration refines the parameter accuracy further, and the process continues for a predetermined number of iterations or until convergence is achieved. This periodic refinement balances the need for high precision with acceptable time consumption by establishing a clear termination criterion.
Data Source
AI summary
This application discloses an equalizer parameter setting method, an audio system, a device, and a readable storage medium. A computing device receives an initial equalizer population; and iteratively selects a first equalizer curve and a second equalizer curve from the initial equalizer population. An audio device selects, based on user preference data, the first equalizer curve or the second equalizer curve as a target equalizer curve. The computing device determines an iterative equalizer population based on the target equalizer curve to acquire an iterative target equalizer curve. After determining a number of iterations reaches a preset value, the computing device determines the final iterative target equalizer curve, and causes the audio device to set the corresponding equalizer parameters based on the final iterative target equalizer curve. This application iteratively selects based on the initial equalizer population, and utilizes a group evolution strategy to achieve a reduction in the search space range, improve convergence speed, and enhance the accuracy of matching between final set equalizer parameters and user preference data.