Neural Audio Equalization by Media Variant and Sound Characteristics
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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 reference audio signals and equalization curves by expert audio engineers, which analyzes incoming audio signals to determine optimal equalization adjustments 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 requiring manual user input. The neural network model self-adjusts the equalization parameters based on the detected genre, era, and mood of the audio signal, making the system self-adaptive and eliminating the need for complex manual configuration interfaces.
Solution Approach 2:
The system dynamically changes equalization parameters (frequency response curves) based on detected audio characteristics. Different genres, eras, and moods trigger different pre-defined equalization curves, allowing the system to adapt to various audio types by modifying key parameters rather than requiring complete system reconfiguration.
2Adaptability or versatility
If manual equalization adjustments are required frequently, then adaptability to audio characteristics is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic equalization adjustment without requiring frequent manual user intervention. The neural network continuously monitors audio characteristics and automatically applies appropriate equalization curves, making the system self-adjusting and eliminating the burden of frequent manual operations while maintaining high adaptability.
Solution Approach 2:
The system implements a feedback loop where the detected audio characteristics (genre, era, mood) continuously inform the equalization settings. This closed-loop approach ensures the system automatically responds to changes in audio content, maintaining optimal settings without user intervention while adapting to different audio types.
3Adaptability or versatility
If dynamic equalization adjustment is implemented, then adaptability to audio characteristics is improved, but device complexity deteriorates
Solution Approach 1:
The system pre-defines multiple equalization curves for different genres, eras, and moods before runtime. The neural network model is pre-trained on reference audio signals and expert audio engineer preferences. This preliminary preparation allows the system to quickly select and apply appropriate curves during operation without performing complex real-time calculations, reducing processing complexity while maintaining adaptability.
Solution Approach 2:
The system segments the audio processing task into distinct components: genre detection, era detection, mood detection, and equalization curve selection. Each component handles a specific aspect of audio characterization, and the results are combined to select the final equalization settings. This segmentation simplifies the overall processing complexity by breaking down the complex adaptation task into manageable modules.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization based on variant selection. An example apparatus to equalize audio includes at least one memory, machine readable instructions, and processor circuity to at least one of instantiate or execute the machine readable instructions to train a neural network model to apply a first audio equalization profile to first audio associated with a first variant of media, and apply a second audio equalization profile to second audio associated with a second variant of media. The processor circuitry is to at least one of instantiate or execute the machine readable instructions to at least one of dispatch or execute the neural network model.