Dynamic Audio Equalization for Smooth Real-Time Volume Correction
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Solution Overview
Problem
Conventional audio equalization settings fail to adapt dynamically to the varying characteristics of different audio sources and genres, requiring frequent manual adjustments by listeners, as they do not account for changes in audio signals over time or between different tracks and sources.
Innovation Solution
The implementation of a neural network-based system that dynamically adjusts audio playback settings by analyzing real-time audio characteristics, using a library of reference media profiles optimized by audio engineers, and applying filters such as low-shelf, peaking, and high-shelf filters, with smoothing techniques to avoid perceptible changes, and thresholding to ensure a smooth equalization curve.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional audio equalization settings are used, then the system is simple and easy to operate, but it fails to adapt dynamically to varying audio characteristics requiring frequent manual adjustments
Solution Approach 1:
The patent implements dynamic equalization by continuously analyzing audio signal characteristics and automatically adjusting filter parameters in real-time. The system transitions from static pre-set equalization to dynamic adaptation by monitoring frequency content, genre, and audio source characteristics, then adjusting low-shelf, high-shelf, and peaking filter parameters accordingly without requiring manual intervention.
Solution Approach 2:
The system performs self-adjustment by automatically analyzing incoming audio signals and modifying equalization settings without user input. The audio processing system serves itself by detecting audio characteristics and autonomously optimizing filter parameters, eliminating the need for frequent manual adjustments while maintaining optimal sound quality across different audio sources and genres.
2Reliability
If manual equalization adjustments are made frequently, then the listening experience can be optimized, but it requires significant user time and effort
Solution Approach 1:
The system implements automatic feedback loops where audio signal characteristics are continuously monitored and fed back to the equalization algorithm. This feedback mechanism enables the system to detect changes in audio content, genre transitions, and source characteristics, then automatically adjust filter parameters to maintain optimal listening quality without requiring user awareness or intervention.
Solution Approach 2:
The equalization system serves itself by autonomously analyzing audio characteristics and adjusting parameters without user involvement. The system performs self-optimization by detecting audio genre, frequency content, and source type, then automatically applying appropriate filter settings, completely eliminating the time users would otherwise spend manually adjusting equalization.
3Extent of automation
If dynamic real-time analysis is implemented, then automated adjustment is achieved, but it increases processing requirements
Solution Approach 1:
The system applies partial analysis by focusing computational resources on the most critical audio characteristics that impact equalization, such as dominant frequency ranges, genre classification, and source identification. Rather than analyzing every aspect of the audio signal in full detail, the system performs selective analysis of key parameters that drive equalization decisions, reducing overall processing energy while maintaining effective automation.
4Manufacturing precision
If aggressive equalization adjustments are applied, then audio characteristics are optimized, but perceptible changes and artifacts may occur
Solution Approach 1:
The system applies moderate, partial adjustments rather than aggressive equalization changes. By making subtle modifications to filter parameters based on audio characteristics, the system achieves optimization while staying below the threshold of perceptibility. The equalization adjustments are calibrated to be just sufficient to correct imbalances without creating noticeable artifacts or distorting the natural sound.
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.