Neural Audio Equalization Using Variant-Based EQ Selection
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Solution Overview
Problem
Conventional audio processing systems struggle to dynamically adjust equalization settings to account for changes in audio characteristics, such as genre, era, and mood, leading to inconsistent listening experiences across different media sources.
Innovation Solution
The implementation of a neural network model trained on audio signals equalized by expert audio engineers, which analyzes incoming audio signals to determine average volume values and standard deviation for frequency ranges, and adjusts equalization settings based on user preferences and audio characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional audio processing systems use fixed equalization settings, then device complexity is reduced, but adaptability to different audio characteristics deteriorates
Solution Approach 1:
The system automatically analyzes audio characteristics and adjusts equalization settings without user intervention. The neural network model self-adapts to different audio sources by detecting genre, era, and mood characteristics, eliminating the need for manual configuration while maintaining high adaptability.
Solution Approach 2:
The system dynamically changes equalization parameters based on detected audio characteristics. By monitoring frequency ranges, volume levels, and standard deviation metrics, the system adjusts EQ settings in real-time to match the optimal profile for each audio source, achieving adaptability through parameter optimization.
2Reliability
If dynamic equalization adjustment is implemented, then listening experience consistency is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of audio characteristics at the beginning of playback or when source changes occur. By pre-detecting genre, era, and mood characteristics before full playback, the system prepares equalization settings in advance, minimizing processing delays during actual music playback while maintaining consistent listening experience.
Solution Approach 2:
The system continuously monitors audio characteristics and provides feedback to the equalization adjustment mechanism. By analyzing real-time data from frequency ranges and volume levels, the system makes incremental adjustments that maintain consistency without requiring complete re-processing, thus reducing time loss while preserving reliability.
3Measurement precision
If neural network model analysis is used, then equalization precision is improved, but computational energy consumption increases
Solution Approach 1:
The system applies partial neural network analysis by focusing on specific frequency ranges and key characteristics rather than processing the entire audio spectrum at full resolution. By selecting critical parameters for analysis (genre, era, mood indicators), the system achieves sufficient precision for equalization adjustment while reducing computational energy requirements compared to exhaustive analysis.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed methods and apparatus for audio equalization based on variant selection. An example apparatus includes a processor to obtain training data, the training data including a plurality of reference audio signals each associated with a variant of music and organize the training data into a plurality of entries based on the plurality of reference audio signals, a training model executor to execute a neural network model using the training data, and a model trainer to train the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.