Neural WDRC Parameter Inference for Diverse Audio Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audio systems struggle to provide optimal audio adjustments for diverse audio sources due to their varying sound characteristics, necessitating a single adjustment that is not universally applicable.
Innovation Solution
An audio parameter optimizing method that extracts sound features, determines wide dynamic range compression (WDRC) parameters, and trains a neural network to generate a parameter inference model for determining appropriate WDRC parameters based on sound characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single audio adjustment is applied to all audio signals, then the device complexity is reduced and ease of operation is improved, but the adaptability to different sound characteristics deteriorates
Solution Approach 1:
The system automatically analyzes sound features and determines appropriate WDRC parameters without requiring manual user configuration. The audio processing system serves itself by autonomously adapting parameters based on real-time sound characteristic analysis, eliminating the need for users to manually adjust settings for different audio sources.
Solution Approach 2:
The audio parameters are made dynamic rather than static, allowing the system to continuously adapt WDRC parameters based on real-time sound feature analysis. The parameters change dynamically in response to different audio signals, enabling the system to optimize performance for each specific audio source while maintaining a unified user interface.
2Adaptability or versatility
If manual audio parameter adjustment is implemented, then adaptability to different audio sources is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic sound feature analysis and parameter determination without requiring user intervention. The audio processing system independently analyzes incoming signals, extracts relevant features, and selects optimal WDRC parameters, completely eliminating manual adjustment operations while maintaining high adaptability.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where sound features are continuously analyzed and the results feed back into parameter selection. This automatic feedback loop ensures that the system continuously optimizes parameters based on actual sound characteristics without requiring user input or manual adjustments.
3Manufacturing precision
If audio parameters are optimized for specific sound characteristics, then sound quality is improved, but device complexity increases
Solution Approach 1:
The audio processing system is segmented into distinct functional modules: sound feature extraction module, parameter determination module, and audio processing module. This segmentation allows each module to specialize in a specific task, achieving high optimization precision while managing complexity through modular design and clear separation of concerns.
Solution Approach 2:
The system performs preliminary sound feature analysis and parameter determination in advance before actual audio processing. By pre-analyzing sound characteristics and pre-determining optimal parameters, the system achieves high optimization precision without adding complexity to the real-time processing path, as the heavy computational work is completed beforehand.
Data Source
AI summary
An audio parameter optimizing method and a computing apparatus related to audio parameters. In the method, sound features of multiple sound signals are obtained. A wide dynamic range compression (WDRC) parameter corresponding to each of the sound signals is determined. Multiple data sets including the sound features and the corresponding WDRC parameters of the sound signals are created. The data sets are used to train a neural network, so as to generate a parameter inference model. The parameter inference model is configured to determine the WDRC parameter of a to-be-evaluated signal. Accordingly, a proper parameter could be provided.