Deep Learning Audio Equalization for Unlabeled Music Preferences
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Solution Overview
Problem
Existing audio balancing methods, such as EQ equalizers, fail to adaptively adjust sound quality to individual user preferences, especially for unlabeled or unknown music styles, as they rely on music tags and cannot meet unique user needs.
Innovation Solution
A deep learning method using neural networks to extract audio features and generate balancing results, combining supervised and unsupervised learning to create a user preference style portrait, which adjusts sound quality by enhancing or attenuating frequency bands based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional EQ equalizer methods are used to adjust audio balancing, then the device complexity is low and ease of operation is high, but the adaptability to individual user preferences and unknown music styles is poor
Solution Approach 1:
The system automatically analyzes user listening behavior and music characteristics to generate personalized EQ settings without requiring manual user input or intervention. The deep learning model self-adjusts the audio balancing parameters based on accumulated user preferences and music features, enabling the system to serve itself in creating optimal audio settings.
Solution Approach 2:
The patent replaces traditional manual EQ adjustment mechanisms with a deep learning-based automatic adjustment system. Instead of relying on mechanical or manual slider adjustments, the system uses neural networks to automatically analyze and adjust audio parameters, substituting the mechanical interaction model with an intelligent algorithmic model.
2Reliability
If music tags are used to determine EQ settings, then the ease of operation is improved, but the reliability fails when music cannot obtain corresponding labels or belongs to unknown styles
Solution Approach 1:
The system performs preliminary analysis of music characteristics and user preferences by pre-training deep learning models on large datasets of music and user behavior. This preliminary action enables the system to handle unknown or unlabeled music by having already learned general patterns and relationships between music features and optimal EQ settings, so it doesn't need explicit tags to function effectively.
Solution Approach 2:
The patent transforms the approach from discrete music tag classification to continuous parameter analysis. Instead of relying on categorical music tags that may not exist for unknown styles, the system analyzes continuous audio features and user behavior parameters, allowing it to generalize to any music style by adjusting parameters based on learned patterns rather than requiring predefined categories.
3Productivity
If manual user adjustment of EQ parameters is implemented, then the ease of operation is high, but the productivity of achieving personalized audio experience is low due to requiring user intervention
Solution Approach 1:
The system automatically adjusts EQ parameters based on user listening behavior and music characteristics without requiring manual user intervention. The deep learning model continuously learns from user preferences and automatically optimizes audio settings, enabling the system to achieve personalized audio experience independently without user effort.
Solution Approach 2:
The system implements a feedback mechanism where user listening behavior, preferences, and responses to different audio settings are continuously collected and used to refine the deep learning model. This feedback loop enables the system to progressively improve its audio balancing recommendations, achieving faster and more accurate personalization over time without requiring explicit user input for each adjustment.
Data Source
AI summary
A deep learning method-based tonal balancing method, apparatus, and system, the method includes: extracting features from audio data to obtain audio data features, generating audio balancing results by using a trained audio balancing model based on the obtained audio data features. The present invention employs deep neural networks and unsupervised deep learning method to solve the problems of audio balancing of unlabeled music and music of unknown style. The present invention also combines user preferences statistics to achieve a more rational multi-style audio balancing design to meet individual needs.