Audio Equalization With Neural Network Smoothing for Source Variations
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Solution Overview
Problem
Conventional audio equalization methods require frequent manual adjustments due to significant differences in audio characteristics between various media sources and genres, leading to an inconsistent listening experience.
Innovation Solution
Dynamic audio playback settings are adjusted in real-time using a neural network trained on reference media, which determines optimal equalization settings based on real-time analysis of audio signals, incorporating smoothing filters to avoid perceptible changes and thresholding to smooth out equalization curves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual equalization adjustments are made for different media sources, then audio characteristics can be optimized, but user convenience deteriorates due to frequent adjustments required
Solution Approach 1:
The system automatically detects media source characteristics and applies appropriate equalization settings without requiring user intervention. The processor analyzes audio signals from different sources (CD, Bluetooth, USB, Wi-Fi, auxiliary inputs) and autonomously selects optimal equalization parameters, allowing the system to serve itself rather than requiring manual user adjustment for each media type.
Solution Approach 2:
The system dynamically changes equalization parameters based on the detected media source characteristics. Different media sources (CD, Bluetooth, USB, Wi-Fi, auxiliary inputs) trigger different equalization settings, allowing the audio output to be optimized for each source type while maintaining consistent user experience across all inputs.
2Manufacturing precision
If equalization settings are optimized for specific media sources, then audio quality improves, but system complexity increases due to multiple settings management
Solution Approach 1:
The system incorporates feedback loops where the processor continuously monitors audio signals from different media sources and automatically adjusts equalization settings based on detected characteristics. This closed-loop approach eliminates the need for manual configuration of multiple settings, as the system self-regulates based on real-time audio analysis, thereby reducing perceived system complexity while maintaining high audio quality.
Solution Approach 2:
The equalization system is designed to handle multiple media sources (CD, Bluetooth, USB, Wi-Fi, auxiliary inputs) through a single unified processor that automatically adapts to different input types. This multi-functional approach consolidates what would otherwise require separate equalization systems for each media type, reducing overall system complexity while maintaining optimized audio quality across all sources.
3Reliability
If real-time audio analysis is performed, then consistent listening experience improves, but processing requirements and energy consumption increase
Solution Approach 1:
The system performs real-time audio analysis at strategically selected intervals rather than continuously processing every audio sample. The processor analyzes key characteristics of incoming audio signals from different media sources and applies equalization settings based on these periodic assessments, achieving consistent listening experience while reducing overall processing energy requirements compared to continuous full-spectrum analysis.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization. Example instructions disclosed herein cause one or more processors to at least: detect an irregularity in a frequency representation of an audio signal in response to a change in volume between a set of frequency values exceeding a threshold; and adjust a volume at a first frequency value of the set of frequency values to reduce the irregularity.