Smart Speaker Equalizer Tuning Using Neural Turning-Point Detection
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Solution Overview
Problem
The manual adjustment of equalizer parameters in smart speakers is inconvenient for ordinary users, and there is no clear method to ensure optimal settings for audio quality.
Innovation Solution
An audio parameter setting method using a neural network model to detect turning points in a smoothed frequency response curve, determining equalizer parameters based on these points and a target frequency response curve to automatically adjust audio settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of equalizer parameters is used, then users can control audio settings, but the operation convenience deteriorates and audio quality optimization becomes difficult
Solution Approach 1:
The system automatically measures the speaker's frequency response and generates optimal equalizer parameters without user intervention. The audio processing device performs self-diagnosis by playing test signals, analyzing the speaker's actual performance, and autonomously determining compensation parameters, thereby resolving the contradiction between ease of operation and audio quality optimization.
Solution Approach 2:
The patent replaces manual mechanical adjustment of equalizer parameters with an automated electronic measurement and calculation system. By using signal processing and mathematical algorithms to analyze frequency response data and compute optimal parameters, the system eliminates the need for manual trial-and-error adjustment while achieving precise audio quality optimization.
2Manufacturing precision
If automated parameter generation is implemented, then audio quality is improved, but system complexity increases
Solution Approach 1:
The patent introduces an audio processing device as an intermediary between the speaker and the user. This intermediary performs the complex tasks of frequency response measurement, analysis, and parameter calculation, shielding the user from complexity while achieving high audio quality. The intermediary handles all automated processes including signal generation, data acquisition, and equalizer parameter determination.
Solution Approach 2:
The system creates a digital model (frequency response curve) that copies the speaker's actual acoustic characteristics. By working with this digital representation rather than directly adjusting physical parameters, the system simplifies the complexity of audio optimization while maintaining high precision in parameter determination.
Data Source
AI summary
An audio parameter setting method and an electronic device are provided. In the method, a sound signal played by the speaker device is obtained. A frequency response curve of the sound signal is generated. The frequency response curve of the sound signal is smoothed to obtain a smoothed response curve of the sound signal. A target frequency response curve is determined according to the sound signal. Multiple turning points of the smoothed response curve of the sound signal are detected by using a neural network model. Equalizer parameters are determined according to the multiple turning points of the smoothed response curve and the target frequency response curve.