Sound Effect Generation Using Machine Learning Aesthetic Extraction
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Solution Overview
Problem
Replicating a specific sound design aesthetic in video games, especially when the original sound designer is not involved, is challenging and requires significant manual labor, as it is difficult to maintain consistency across different contexts within the game.
Innovation Solution
A computer-implemented method that characterizes a sound design aesthetic by determining common effect characteristics from a set of obtained sounds and applies these characteristics to a base sound using machine learning models, allowing for the generation of new sounds or modification of existing ones to match the desired aesthetic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual sound design is used to replicate aesthetic, then sound quality can be maintained, but production time and labor cost increase significantly
Solution Approach 1:
The patent uses machine learning models to copy and replicate sound design aesthetics automatically. The system analyzes reference sounds to extract characteristic features and generates new sounds that replicate the desired aesthetic without manual intervention, directly addressing the time consumption issue while maintaining quality consistency
Solution Approach 2:
The patent replaces the mechanical manual process of sound design with an automated computer system using machine learning algorithms. The system substitutes human expertise with computational models that can generate sounds consistently, eliminating the time loss associated with manual replication while preserving aesthetic quality
2Manufacturing precision
If manual sound design is used to maintain aesthetic consistency, then sound quality is preserved, but device complexity and operational burden increase
Solution Approach 1:
The system performs self-service by automatically analyzing reference sounds, extracting aesthetic characteristics, and generating new sounds without requiring manual intervention. The machine learning models independently complete the sound design process, reducing operational complexity while maintaining aesthetic consistency
Solution Approach 2:
The patent replaces complex manual operational processes with automated machine learning systems. The computational models handle the complexity of aesthetic analysis and sound generation, eliminating the need for manual expertise and reducing operational burden while preserving sound quality
3Productivity
If automated sound generation is used, then production efficiency improves, but difficulty in achieving desired aesthetic precision increases
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning models continuously refine their sound generation based on analysis of reference sounds. The system learns from the aesthetic characteristics of reference audio and adjusts its generation process to achieve precise aesthetic accuracy while maintaining high production efficiency
Solution Approach 2:
The patent utilizes parameter changes in machine learning models to achieve precise aesthetic control. The system adjusts various acoustic parameters and model configurations to match the desired aesthetic characteristics, enabling both high productivity and precise aesthetic accuracy through computational optimization
Data Source
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AI summary
A computer-implemented method of generating a sound effect, comprising: obtaining a plurality of sounds; determining a common effect characteristic of the plurality of obtained sounds; obtaining a base sound; and generating the sound effect by applying the common effect characteristic to the base sound.