Music Signal Equalizer Using CNN-Based Automatic EQ Generation
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Solution Overview
Problem
Users find it inconvenient and time-consuming to manually set equalizer effects for music signals, especially when they lack clear tone criteria, in existing music reproduction systems.
Innovation Solution
An equalizer system that uses a convolutional neural network to analyze music signals, calculate probability values for attributes like loudness, tone, and rhythm complexity, and automatically generate an EQ value by combining a general-purpose EQ with attribute-specific gains, applying it to improve sound quality and usability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the user manually selects and sets the EQ effect, then the user can control the tone preference, but it consumes extra effort and time for normal users
Solution Approach 1:
The equalizer automatically analyzes music signal attributes (loudness, tone, rhythm complexity) and generates appropriate EQ values without user intervention. The system serves itself by autonomously determining the optimal equalization settings based on the detected music characteristics, eliminating the need for manual user configuration
Solution Approach 2:
The system pre-calculates and stores EQ values for multiple music attributes before playback. When music is played, the system quickly retrieves and applies the pre-computed EQ settings based on real-time attribute analysis, avoiding the time-consuming manual adjustment process
2Adaptability or versatility
If the user manually tunes the EQ effect, then the user can achieve personalized tone, but it requires clear criteria of tones which normal users lack
Solution Approach 1:
The patent replaces the manual mechanical process of EQ adjustment with an automated digital signal processing system. A convolutional neural network analyzes music attributes and automatically generates EQ values, substituting the user's manual tuning process with an intelligent algorithm that objectively determines optimal settings based on detected music characteristics
Solution Approach 2:
The system introduces an intermediate processing layer (attribute analysis and EQ generation module) between the music signal and the equalization output. This intermediary automatically interprets music characteristics and translates them into appropriate EQ settings, eliminating the need for users to directly understand and adjust complex tone parameters
3Extent of automation
If automatic EQ generation is implemented, then user effort is reduced, but the system complexity increases with neural network processing
Solution Approach 1:
The patent segments the EQ generation process into distinct functional modules: music attribute detection, probability calculation for multiple attributes, moderate index computation, and final EQ value generation. This modular segmentation allows each component to be optimized independently and simplifies the overall system architecture despite the automated processing requirements
Data Source
AI summary
An equalizer and a method of controlling same are provided. The equalizer includes a memory storing an EQ value set for a plurality of music attributes and storing a general-purpose EQ value; and a processor configured to: obtain an input music signal; calculate a plurality of probability values for the plurality of music attributes by analyzing attributes of the input music signal based on a convolutional neural network; calculate a moderate index between the plurality of probability values; generate an EQ value based on the plurality of probability values and the moderate index; and perform equalizing by applying the generated EQ value to the input music signal.