Neural Audio Equalization Using Variant Selection and Style Blending
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Solution Overview
Problem
Conventional audio processing systems face challenges in dynamically adjusting equalization settings to account for varying audio characteristics from different sources and genres, leading to inconsistent listening experiences for users.
Innovation Solution
The implementation of a neural network model that analyzes incoming audio signals to determine average volume and standard deviation values across frequency ranges, using a one-hot matrix to adjust equalization settings based on user preferences, allowing for seamless adaptation to changes in audio characteristics without requiring separate models for each genre or engineer style.
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 sources and genres deteriorates
Solution Approach 1:
The system dynamically adjusts equalization settings by analyzing audio characteristics of incoming signals and automatically selecting appropriate equalization curves from a library, transforming fixed settings into adaptive, variable settings that respond to different audio sources and genres
Solution Approach 2:
The system performs self-adjustment by automatically analyzing audio input characteristics and selecting appropriate equalization parameters without requiring manual user intervention, enabling the system to serve itself in adapting to different audio sources
2Adaptability or versatility
If separate neural network models are created for each genre or engineer style, then adaptability to different music types improves, but device complexity and processing power requirements worsen
Solution Approach 1:
A single neural network model is designed to perform multiple functions by processing different audio genres and styles through unified architecture, eliminating the need for separate models for each genre while maintaining adaptability across diverse music types
Solution Approach 2:
The system achieves genre-specific processing by dynamically changing parameters such as equalization curve selection and processing thresholds based on detected audio characteristics, rather than using separate models for each genre
3Manufacturing precision
If manual equalization adjustments are required for each source change, then manufacturing precision of audio quality is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically analyzes incoming audio signals and adjusts equalization settings without requiring manual user intervention, performing the adjustment service itself based on detected audio characteristics and user preferences
4Adaptability or versatility
If dynamic equalization adjustment is implemented, then adaptability to audio characteristics improves, but processing power requirements worsen
Solution Approach 1:
The system performs partial analysis by focusing on key audio characteristics such as frequency spectrum and temporal patterns rather than processing all possible audio parameters, achieving sufficient adaptability with reduced processing power consumption
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed methods and apparatus for audio equalization based on variant selection. An example apparatus includes a processor to obtain training data, the training data including a plurality of reference audio signals each associated with a variant of music and organize the training data into a plurality of entries based on the plurality of reference audio signals, a training model executor to execute a neural network model using the training data, and a model trainer to train the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.