Automated Sound Mix Versioning via Machine Learning
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Solution Overview
Problem
The current manual process of creating different versions of sound mixes for various distribution channels is time-consuming, reduces creative collaboration between sound mixers and filmmakers, introduces potential errors, and is costly due to the need for multiple equipment configurations and quality control passes, while also being unable to predict every potential destination format, especially with the rise of personalized audience experiences.
Innovation Solution
A machine learning-based system that extracts metadata and audio feature data from an original sound mix to automatically calculate and derive new versions, using a trained model to adjust audio levels, spectral balance, and content identities, allowing for automated sound mix versioning and continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual process is used to create different versions of sound mixes, then quality control and creative collaboration can be maintained, but the process becomes time-consuming and reduces productivity
Solution Approach 1:
The system creates derivative sound mixes by copying and transforming an original sound mix using machine learning models. The ML model learns from the original mix and generates multiple derivative versions automatically, replacing manual copying and adaptation processes while maintaining quality through learned patterns from training data.
Solution Approach 2:
The patent replaces the mechanical manual process of sound mixing with an automated machine learning system. The ML model automatically analyzes the original sound mix, extracts features, and generates derivative mixes without requiring manual intervention for each version, thus increasing productivity while maintaining quality through algorithmic consistency.
2Adaptability or versatility
If multiple equipment configurations are used for different distribution channels, then format adaptability is improved, but device complexity and costs increase
Solution Approach 1:
The machine learning system serves as a universal platform that can generate sound mixes for multiple distribution channels and formats from a single original mix. The ML model is trained on diverse data and can adapt to different target formats (theatrical, broadcast, streaming, etc.) without requiring separate dedicated equipment for each channel, thus reducing overall system complexity.
Solution Approach 2:
The system adapts sound mixes for different distribution channels by changing audio parameters such as loudness, frequency balance, and spatial characteristics through ML-driven processing. Instead of using different physical equipment configurations, the system modifies digital audio parameters to suit each target format, reducing hardware complexity while maintaining format adaptability.
3Measurement precision
If manual quality control passes are performed for each sound mix version, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The machine learning system performs quality control continuously and automatically as part of the derivative mix generation process. The ML model continuously monitors and adjusts audio features during the mixing process, eliminating the need for separate discrete quality control passes for each version, thus reducing time loss while maintaining precision through consistent algorithmic application.
Solution Approach 2:
The system performs self-quality control through the machine learning model, which automatically evaluates and adjusts the generated derivative mixes based on learned quality criteria. The ML model serves as its own quality control mechanism, eliminating the need for external manual inspection while maintaining measurement precision through trained evaluation metrics.
4Productivity
If automated machine learning process is used for sound mix versioning, then productivity and time efficiency are improved, but manufacturing precision may be compromised
Solution Approach 1:
The system performs preliminary training of the machine learning model using extensive datasets of high-quality sound mixes before actual production. This preliminary training phase establishes accurate reference patterns and quality standards that the model will apply during automated derivative mix generation, ensuring manufacturing precision is maintained while enabling high-speed automated production.
Solution Approach 2:
The machine learning system incorporates feedback mechanisms where the model continuously evaluates its own output and adjusts parameters to maintain audio quality. The feedback loop compares generated derivative mixes against target specifications and learned quality patterns, automatically correcting deviations to preserve manufacturing precision throughout the high-speed automated process.
Data Source
AI summary
Implementations of the disclosure describe systems and methods that leverage machine learning to automate the process of creating various versions of sound mixes using an original sound mix as a starting point. In implementations, a system for automated versioning of sound mixes may include: (i) a component to extract metadata categorizing/identifying the input sound mix; (ii) a component to extract audio features of the input sound mix; (iii) a component that uses a machine learning model to compare the extracted audio features of the input sound mix with extracted audio features of previously analyzed sound mixes to calculate audio features of a target sound mix; and (iv) a component to perform signal processing to derive the target sound mix given the calculated audio features.