Neural Audio Equalization Using Variant Selection for Source Changes
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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 and expert audio engineer styles, allowing for seamless adaptation to changes in audio characteristics.
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 automatically analyzes incoming audio signals and adjusts equalization settings without requiring manual user input. The neural network model self-adapts to different audio sources and genres by processing audio characteristics and selecting appropriate equalization curves, enabling the system to serve itself in optimizing audio output.
Solution Approach 2:
The system dynamically changes equalization parameters (frequency response characteristics) based on analyzed audio signal properties. By modifying equalization curve parameters according to detected audio characteristics, the system adapts to different sources and genres while maintaining manageable complexity through parameterized adjustments rather than structural changes.
2Adaptability or versatility
If conventional audio processing systems manually adjust equalization settings for each source, then adaptability improves, but ease of operation deteriorates
Solution Approach 1:
The system automatically performs equalization adjustment without requiring user intervention. The neural network model autonomously analyzes audio characteristics and selects appropriate equalization settings, eliminating the need for users to manually adjust settings for different sources while maintaining high adaptability.
Solution Approach 2:
The system continuously monitors audio signal characteristics and uses this feedback to dynamically adjust equalization settings. By establishing a feedback loop that detects audio properties and automatically modifies equalization in response, the system achieves adaptability without requiring user operation.
3Adaptability or versatility
If multiple neural network models are used for different audio sources, then adaptability improves, but device complexity increases
Solution Approach 1:
A single neural network model is designed to handle multiple audio sources and genres universally. The model incorporates multiple equalization curves and selection mechanisms that allow it to perform the functions of multiple specialized models, reducing overall system complexity while maintaining comprehensive adaptability across different audio sources.
Solution Approach 2:
The patent combines multiple equalization curves and processing capabilities into a single integrated neural network model. By merging what would otherwise require separate models into one unified system with curve selection and blending capabilities, the patent reduces device complexity while preserving the ability to handle diverse audio sources effectively.
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.