Audio Equalization Variant Selection With Unified Neural Profiles
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Solution Overview
Problem
Conventional audio processing systems face challenges in dynamically adjusting equalization settings to account for changes in audio characteristics across different media sources and genres, leading to inconsistent listening experiences.
Innovation Solution
A neural network-based system that analyzes incoming audio signals to determine average volume and standard deviation values across frequency ranges, using a single neural network model trained on various audio signals and equalization curves to dynamically adjust settings based on user preferences, incorporating a one-hot matrix to weight preferences for specific audio engineers and genres.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate neural network models are used for different audio engineers and genres, then customization and user preference accuracy are improved, but processing resources and computational complexity increase
Solution Approach 1:
The patent combines multiple separate neural network models into a single unified model that processes all audio signals regardless of engineer or genre. The model receives audio input and uses internal processing to dynamically determine appropriate equalization settings, eliminating the need for separate models while maintaining the ability to handle different audio characteristics through a shared architecture
Solution Approach 2:
The single neural network model is designed to be universal, capable of handling multiple audio engineers, genres, and signal types through one unified system. The model learns from diverse training data encompassing various audio characteristics and applies this knowledge broadly, making the system multi-functional without requiring separate specialized models for each audio type
2Reliability
If dynamic equalization adjustment is implemented to adapt to changing audio characteristics, then listening experience consistency is improved, but processing complexity and computational load increase
Solution Approach 1:
The system implements dynamic equalization adjustment by continuously analyzing incoming audio signal characteristics and automatically modifying equalization parameters in real-time. The neural network monitors audio input and adapts equalization settings dynamically based on detected signal properties, enabling the system to respond to changing audio conditions without manual intervention
Solution Approach 2:
The neural network performs self-adjustment of equalization settings by autonomously analyzing audio characteristics and determining appropriate processing parameters. The system serves itself by automatically adapting to different audio sources and genres without requiring external control or complex processing architecture, reducing overall system complexity while maintaining adaptability
3Measurement precision
If comprehensive audio analysis across multiple frequency ranges is performed, then equalization accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments the audio frequency spectrum into multiple discrete frequency ranges, analyzing each segment separately through dedicated processing pathways in the neural network. This segmentation allows the model to handle complex multi-frequency analysis by breaking it into manageable portions that can be processed efficiently in parallel, maintaining accuracy while improving processing throughput
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 circuitry 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.