Neural Formant Control for Loudness-Aware Spectral Balance
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Solution Overview
Problem
There is a need for a computer-implemented aid in music production to automatically optimize formant attenuation or amplification in audio processing, as existing technologies lack efficient methods for adjusting audio frequencies based on overall loudness and spectral profiles.
Innovation Solution
A method using a neural network to determine feature values of an input audio window and calculate a formant attenuation/amplification coefficient, which is then applied to adjust the formants while conserving the overall spectral profile, utilizing equal-loudness-level contours and power spectrum analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual formant adjustment is performed in audio processing, then audio quality can be optimized, but the complexity of operation increases and productivity decreases
Solution Approach 1:
The system performs automatic formant detection and adjustment without requiring manual user intervention. The neural network autonomously analyzes the audio signal, identifies formant frequencies, and applies appropriate attenuation or amplification coefficients, allowing the system to serve itself rather than requiring continuous user operation.
Solution Approach 2:
The patent replaces manual mechanical adjustment operations with an automated neural network-based system. The neural network processes audio features and automatically determines formant coefficients, substituting the need for manual slider adjustments and graphical equalizer operations with intelligent automated processing.
2Productivity
If automatic formant adjustment is implemented using traditional methods, then productivity increases, but manufacturing precision and audio quality optimization deteriorate
Solution Approach 1:
Traditional automatic formant detection methods are replaced with a neural network-based system that leverages machine learning to achieve superior detection accuracy. The neural network processes spectral features and temporal patterns to identify formants with higher precision than conventional algorithmic approaches, maintaining productivity while improving quality.
Solution Approach 2:
The system changes the approach to formant detection by using neural network parameters and learned features instead of fixed algorithmic parameters. The neural network adapts to different audio characteristics and automatically adjusts detection sensitivity, enabling high precision across diverse audio content while maintaining efficient processing speeds.
3Speed
If formant manipulation is performed without considering overall loudness, then processing speed increases, but audio quality and spectral profile conservation deteriorate
Solution Approach 1:
The system performs preliminary analysis of the overall audio loudness and spectral characteristics before applying formant manipulation. The neural network evaluates the global audio properties first, then uses this information to guide formant-specific adjustments, ensuring that formant manipulation is contextually appropriate and conserves the overall spectral profile while maintaining processing efficiency.
Data Source
AI summary
A method comprising determining feature values of an input audio window and determining a formant attenuation/amplification coefficient for the input audio window based on the processing of the feature values by a neural network.