Neural Audio Equalization With EQ Curve Smoothing
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Solution Overview
Problem
Conventional audio equalization methods require frequent manual adjustments due to varying audio characteristics across different media sources and genres, leading to an inconsistent listening experience.
Innovation Solution
A system that dynamically adjusts audio playback settings using a neural network trained on reference media, applying filters such as low-shelf, peaking, and high-shelf filters, and employs smoothing techniques to maintain a consistent equalization curve, eliminating the need for user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual equalization adjustments are made for different media sources and genres, then audio quality can be optimized for specific sources, but user convenience deteriorates due to frequent manual adjustments
Solution Approach 1:
The system automatically detects media source characteristics and applies appropriate equalization settings without user intervention. The neural network analyzes audio signals in real-time and adjusts equalization parameters autonomously, allowing the system to serve itself rather than requiring manual user adjustments for different media sources and genres.
Solution Approach 2:
The system dynamically changes equalization parameters based on detected media characteristics. By monitoring audio signal properties and automatically adjusting frequency response parameters, the system adapts to different media sources and genres while maintaining optimal audio quality without requiring manual user input.
2Stability of the object's composition
If equalization settings are changed frequently to match different media characteristics, then audio consistency across sources improves, but system stability deteriorates due to rapid parameter changes
Solution Approach 1:
The system implements dynamic equalization that adapts to changing media characteristics while maintaining stability through controlled transitions. The neural network continuously monitors audio signals and makes gradual, smooth adjustments to equalization parameters, allowing the system to respond dynamically to different media sources without causing abrupt or destabilizing changes.
Solution Approach 2:
The system uses real-time feedback from audio signal analysis to adjust equalization settings. By continuously monitoring the audio output and comparing it against target characteristics, the system makes incremental adjustments that maintain stability while achieving consistency across different media sources.
3Ease of operation
If automated equalization is implemented using neural networks, then user convenience improves by eliminating manual adjustments, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system replaces manual mechanical adjustment mechanisms with automated neural network-based processing. Instead of requiring physical or manual control of equalization parameters, the system uses machine learning models that automatically analyze audio signals and adjust settings, substituting complex automated processing for simple manual operations.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization. Example apparatus disclosed herein include a volume adjuster to apply equalization adjustments to an audio signal to generate an equalized audio signal, the equalization adjustments output from a neural network in response to an input feature set; a thresholding controller to: detect an irregularity in a frequency representation of the audio signal after application of the equalization adjustments, the irregularity corresponding to a change in volume between adjacent frequency values exceeding a threshold; and adjust a volume at a first frequency value of the adjacent frequency values to reduce the irregularity; an equalization (EQ) curve generator to generate an EQ curve to apply to the audio signal when the irregularity has been reduced; and a frequency to time domain converter to output the equalized audio signal in a time domain based on the EQ curve.