Automated Audio Timbre Conversion With Neural Feature Extraction
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Solution Overview
Problem
Manual timbre conversion in audio processing is inefficient and heavily influenced by human subjectivity, leading to unsatisfactory results.
Innovation Solution
An audio processing method involving timbre and audio feature extraction using neural networks and transformer models to automatically convert audio timbre, utilizing convolutional neural networks and attention mechanisms for enhanced timbre feature representation and conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual timbre conversion is used, then human subjective control is maintained, but conversion efficiency is low and human subjective influence is serious
Solution Approach 1:
The patent replaces the manual mechanical audio editing process with an automated neural network system. The timbre conversion is achieved through deep learning models that automatically extract timbre features and perform conversion, eliminating the need for manual waveform editing while significantly improving conversion efficiency and reducing human subjective influence.
Solution Approach 2:
The system enables self-service timbre conversion where the neural network automatically processes the conversion without human intervention. The model extracts features, performs transformation, and generates the converted audio autonomously, allowing the system to serve itself in the timbre conversion task while maintaining consistent quality.
2Manufacturing precision
If manual timbre conversion is used, then human auditory perception is directly applied, but conversion accuracy is reduced due to human subjective influence
Solution Approach 1:
The patent replaces human auditory perception with automated acoustic measurement and analysis systems. Neural networks objectively extract timbre features from audio signals and perform precise transformations, eliminating human subjective variations and improving conversion accuracy through consistent, repeatable automated processing.
3Productivity
If automated timbre conversion using neural networks is implemented, then conversion efficiency is improved and human subjective influence is reduced, but system complexity increases
Solution Approach 1:
The patent segments the timbre conversion system into distinct functional modules: audio feature extraction, timbre feature extraction, transformation processing, and audio generation. This modular segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high conversion efficiency.
Solution Approach 2:
The neural network model is designed with multi-functionality to handle various timbre conversion tasks. The same base model can be applied to different audio types and timbre transformations by adjusting input parameters and training data, reducing the need for multiple specialized systems and thereby lowering overall system complexity.
Data Source
AI summary
An audio processing method is applied to an electronic device and includes obtaining target audio with a first timbre, and obtaining object audio of a target object; performing timbre feature extraction on the object audio, to obtain a timbre feature of the object audio, the timbre feature being configured for representing a second timbre of the target object that is different from the first timbre; performing audio feature extraction on the target audio, to obtain an audio feature of the target audio; and generating target audio with the second timbre based on the timbre feature and the audio feature.