Two-Stage Timbre Conversion Model With Diffusion Feature Training
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Solution Overview
Problem
Conventional methods fail to accurately and efficiently convert the timbre of audio within a predetermined time and require extensive training with large amounts of audio data.
Innovation Solution
A method involving the processing of first and second audio content with a first generation model to generate third audio content, providing this content to a second generation model to generate audio features, and training the second model based on these features, utilizing diffusion models for improved timbre conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used for timbre conversion, then the model can process audio content, but the conversion accuracy is insufficient and extensive training with large amounts of audio data is required
Solution Approach 1:
The patent segments the timbre conversion process into two distinct models: a first generation model for initial timbre conversion and a second generation model for refining and reconstructing audio features. This segmentation allows each model to specialize in specific aspects of timbre conversion, improving overall accuracy without requiring extensive training data for a single comprehensive model
Solution Approach 2:
The patent introduces audio features as an intermediary representation between the input audio content and the final converted output. The second generation model processes these intermediate features to reconstruct the audio, enabling more precise timbre control and better conversion accuracy with reduced training data requirements
2Productivity
If conventional methods are used for timbre conversion, then the model can generate converted audio, but the conversion efficiency is low and cannot complete within a predetermined time
Solution Approach 1:
The patent performs preliminary timbre conversion using the first generation model to generate intermediate audio content and features before the second generation model refines the output. This preliminary action allows the system to quickly generate a baseline conversion that can be subsequently improved, reducing overall processing time
Solution Approach 2:
By dividing the conversion process into two sequential stages with different computational complexities, the system can optimize each stage for speed and accuracy respectively, improving overall conversion efficiency while meeting time constraints
3Measurement precision
If a single generation model is used, then the system structure is simple, but the generation effect and timbre conversion accuracy are insufficient
Solution Approach 1:
The patent divides the single model into two specialized generation models: the first model handles initial timbre conversion while the second model focuses on audio feature reconstruction. This functional segmentation improves generation accuracy by allowing each model to specialize in specific tasks
Solution Approach 2:
The patent creates a second generation model that copies and builds upon the architecture and learning of the first model, then specializes it for feature reconstruction. This copying approach allows the system to leverage the first model's learned representations while adding specialized capabilities without completely redesigning the system
Data Source
AI summary
A method, an apparatus, a device and a storage medium for training a generation model are provided. The method provided by the disclosure includes: obtaining first audio content corresponding to a first timbre and second audio content corresponding to a second timbre; processing the first audio content and the second audio content with a first generation model to generate third audio content; providing the third audio content and a first portion of the second audio content to a second generation model to generate a first audio feature; and training the second generation model based on the first audio feature and a second audio feature corresponding to the second audio content.