Audio Equalization Variant Selection for Adaptive Sound Profiles
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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 that analyzes incoming audio signals to determine average volume values and standard deviation across frequency ranges, allowing for dynamic equalization adjustments based on user preferences and audio characteristics, using a single neural network model trained on a variety of reference audio signals and equalization curves.
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-adapts to different audio sources by processing incoming audio signals and generating appropriate equalization curves, enabling the system to serve itself in optimizing audio output across varying media types and sources
Solution Approach 2:
The system dynamically changes equalization parameters based on analyzed audio characteristics. By modifying frequency response parameters, volume levels, and tonal balance in real-time according to the detected audio source properties, the system achieves high adaptability while managing complexity through parameter-based control rather than hardware 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. By continuously monitoring incoming audio signals and autonomously modifying equalization settings, the system eliminates the need for users to repeatedly adjust controls, thereby maintaining high adaptability while significantly improving ease of operation
Solution Approach 2:
The system implements a feedback loop where equalization settings are continuously adjusted based on real-time analysis of audio characteristics. This closed-loop control ensures optimal equalization is maintained automatically as audio sources change, removing the burden of manual adjustments from the user while preserving adaptive optimization
3Device complexity
If a single neural network model is used for multiple audio types, then device complexity is reduced, but manufacturing precision of audio quality deteriorates
Solution Approach 1:
A single neural network model is designed to handle multiple audio types and sources through universal feature extraction and adaptive processing. The model incorporates multiple input channels and conditional processing that enable it to specialize in different audio genres, formats, and sources while maintaining a unified architecture, thus reducing complexity while preserving the capability to deliver high-quality results across diverse audio applications
Solution Approach 2:
The neural network applies different processing strategies to different frequency ranges and audio characteristics within the same model. By implementing localized optimization for specific frequency bands and audio types through attention mechanisms and conditional computation, the model achieves high precision for various audio qualities without requiring separate specialized models, thereby maintaining both simplicity and effectiveness
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.