Adaptive EQ Filtering for Consistent Audio Playback Settings
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Solution Overview
Problem
Conventional audio playback systems require frequent manual adjustments of equalization settings due to varying audio characteristics across different media sources and genres, leading to an inconsistent listening experience.
Innovation Solution
A system that dynamically adjusts audio playback settings, such as equalization and volume, using a neural network trained on reference media to analyze real-time audio characteristics, applying filters and smoothing techniques to maintain optimal settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustments of equalization settings are used, then audio playback can be customized for different media sources, but frequent user intervention is required leading to inconsistent listening experience
Solution Approach 1:
The system automatically analyzes audio characteristics and adjusts equalization settings without requiring user intervention. The neural network model processes audio signals in real-time and dynamically modifies playback parameters, enabling the system to serve itself rather than relying on manual user adjustments for each media source or genre change.
Solution Approach 2:
The equalization settings are made dynamic rather than static, allowing the system to continuously adapt playback parameters based on real-time audio analysis. The neural network enables smooth transitions between different equalization configurations as audio characteristics change, maintaining optimal settings without requiring manual reconfiguration.
2Manufacturing precision
If manual equalization adjustments are made for different media sources, then audio quality can be optimized, but the system complexity increases due to multiple configuration requirements
Solution Approach 1:
The system implements a feedback loop where the neural network continuously analyzes audio characteristics and automatically adjusts equalization settings accordingly. This closed-loop approach eliminates the need for manual configuration management across different media sources, as the system self-corrects and optimizes audio quality based on real-time analysis of the incoming audio signal's genre, frequency distribution, and other characteristics.
Solution Approach 2:
The system dynamically changes equalization parameters based on audio analysis rather than requiring pre-configured settings for each media source. The neural network modifies frequency response curves, gain levels, and other audio parameters in real-time according to the detected audio characteristics, simplifying the system by replacing multiple static configurations with a single adaptive parameter adjustment mechanism.
3Reliability
If dynamic adjustment of audio settings is implemented, then consistent listening experience is achieved, but processing time and computational resources increase
Solution Approach 1:
The neural network model is pre-trained on extensive audio data during an offline training phase, allowing it to quickly recognize and appropriately respond to different audio genres and characteristics during real-time playback. This preliminary training enables the system to make accurate audio adjustments with minimal processing time during actual use, as the complex decision-making logic has already been established in advance.
Solution Approach 2:
The system applies equalization adjustments selectively based on the detected audio characteristics rather than continuously modifying all parameters at maximum intensity. The neural network determines the appropriate level and type of adjustment needed for each specific audio segment, avoiding unnecessary processing overhead while maintaining consistent listening experience through targeted, intelligent modifications.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to adjust audio playback settings based on analysis of audio characteristics. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal; an EQ filter settings analyzer to: access a plurality of audio playback settings determined by the neural network based on the query; and determine a filter coefficient to apply to the audio signal based on the plurality of audio playback settings; and an EQ adjustment implementer to apply the filter coefficient to the audio signal in a first duration.