Automated Sound Mix Versioning via Trained ML Models
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Solution Overview
Problem
Current methods for creating multiple versions of sound mixes for various distribution channels are manual, time-consuming, and prone to errors, reducing the time for creative collaboration between sound mixers and filmmakers, and are costly due to the need for multiple equipment configurations to test each version, while also being unable to predict all potential destination formats, especially with the rise of personalized audience experiences.
Innovation Solution
A system that uses machine learning to automate the process of creating sound mix versions by leveraging historical mixing console data to train models, which generate mixing console features for different formats, allowing for efficient derivation of sound mixes that can be fine-tuned by human mixers and potentially fully automated, enabling faster and more accurate sound mix creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual methods are used to create multiple versions of sound mixes, then quality control and creative adjustment are possible, but the process is time-consuming and reduces time for creative collaboration
Solution Approach 1:
The system performs preliminary automated generation of multiple sound mix versions using machine learning models trained on historical mixing console data. This preliminary action creates initial versions that can then be reviewed and adjusted by sound mixers, significantly reducing the time required for creative collaboration while maintaining quality control.
Solution Approach 2:
The system creates copies of the master sound mix in various destination formats using trained machine learning models that predict mixing console features. These copies are generated automatically with high fidelity, reducing the need for manual recreation while allowing quality verification and adjustment by professionals.
2Measurement precision
If multiple equipment configurations are used to test each sound mix version, then accuracy and quality verification are improved, but costs increase
Solution Approach 1:
The system replaces physical equipment configurations with a virtual machine learning-based prediction system. The trained models simulate mixing console behavior and predict audio features for different destination formats, eliminating the need for multiple physical equipment setups while maintaining verification accuracy through computational modeling.
Solution Approach 2:
The machine learning model acts as an intermediary between the master sound mix and the various destination formats. Instead of directly testing each format with physical equipment, the model predicts the required mixing console features and generates appropriate versions, reducing equipment needs while maintaining quality through intelligent mediation.
3Adaptability or versatility
If manual creation methods are used, then flexibility for creative adjustment is maintained, but productivity and speed of sound mix creation decrease
Solution Approach 1:
The system performs preliminary automated generation of multiple sound mix versions in various destination formats using machine learning models. This creates a foundation of pre-generated versions that maintain creative flexibility through subsequent human review and adjustment, while dramatically increasing creation speed by handling the initial generation automatically.
Solution Approach 2:
The system enables semi-automated sound mix creation where the machine learning model generates versions independently based on trained predictions of mixing console features. Sound mixers can then review and make creative adjustments as needed, maintaining flexibility while the system handles the productive workload of generating multiple versions across different formats.
4Reliability
If traditional methods are used, then existing formats are covered, but inability to predict personalized audience experiences and new formats occurs
Solution Approach 1:
The machine learning model is trained on historical mixing console data from multiple sources and formats, enabling it to generalize and predict mixing console features for both existing and new destination formats. This universal approach maintains reliability for established formats while providing the adaptability to handle personalized experiences and emerging formats that traditional methods cannot predict.
Solution Approach 2:
The system uses parameter-based machine learning models that learn from historical data to predict mixing console features for various destination formats. By changing the input parameters (destination format specifications) to the trained model, it can generate appropriate sound mix versions for both traditional and personalized/new formats without requiring separate manual processes for each.
Data Source
AI summary
Some implementations of the disclosure relate to using a model trained on mixing console data of sound mixes to automate the process of sound mix creation. In one implementation, a non-transitory computer-readable medium has executable instructions stored thereon that, when executed by a processor, causes the processor to perform operations comprising: obtaining a first version of a sound mix; extracting first audio features from the first version of the sound mix obtaining mixing metadata; automatically calculating with a trained model, using at least the mixing metadata and the first audio features, mixing console features; and deriving a second version of the sound mix using at least the mixing console features calculated by the trained model.