Neural EQ Playback Adjustment for Consistent Audio Sources
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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 sources and genres, leading to an inconsistent listening experience.
Innovation Solution
A system that dynamically adjusts audio playback settings in real-time using a neural network trained on reference media, applying filters and smoothing techniques to maintain optimal equalization based on the analysis of audio signals, eliminating the need for user input.
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 sources and genres, but frequent user intervention is required and listening experience remains inconsistent
Solution Approach 1:
The system automatically analyzes audio characteristics and adjusts equalization settings without user intervention. The media unit independently processes audio signals from different sources and genres, applying appropriate equalization profiles based on real-time analysis, thereby eliminating the need for manual adjustments while maintaining adaptability across diverse audio content
Solution Approach 2:
The system continuously monitors audio characteristics and uses this feedback to dynamically adjust equalization settings. By analyzing the audio signal properties and comparing them against reference profiles, the system automatically modifies playback parameters to optimize sound quality for each specific source and genre, creating a closed-loop control system that adapts in real-time
2Reliability
If equalization settings are adjusted for each audio source and genre, then listening experience is optimized, but the system requires complex manual configuration
Solution Approach 1:
Equalization profiles for different sources and genres are pre-configured and stored in the system. When audio playback begins, the system automatically identifies the source type and genre, then retrieves and applies the corresponding pre-prepared equalization profile, eliminating the need for users to manually configure complex settings while ensuring consistent optimized playback across all audio content
Solution Approach 2:
The system dynamically selects and switches between different equalization profiles based on real-time analysis of audio characteristics. Rather than requiring manual configuration, the system automatically adapts its equalization settings by transitioning between pre-defined profiles that are optimized for specific sources and genres, maintaining reliable consistent performance across diverse audio content
3Extent of automation
If automatic audio analysis is implemented, then equalization settings are adjusted in real-time, but processing complexity increases
Solution Approach 1:
The audio analysis process is divided into distinct segments: source identification, genre classification, and equalization profile selection. The media unit processes audio characteristics in separate stages, analyzing specific parameters independently and combining results to determine the appropriate equalization settings, thereby managing processing complexity through structured modular analysis
Data Source
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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 implementor to apply the filter coefficient to the audio signal in a first duration.