Lucasfilm Entertainment Company Ltd.

Lucas Entertainment Company Ltd. LLC

Scott Levine

Stephen Morris

Nicolas R Tsingos

Nicolas Tsingos

Some implementations of the disclosure relate to a non-transitory computer-readable medium having executable instructions stored thereon that, when executed by a processor, cause a system to perform operations comprising: obtaining a first energy-based target for audio; obtaining a first version of a sound mix including one or more audio components; computing, for each audio frame of multiple audio frames of each of the one or more audio components, a first audio feature measurement value; optimizing, based at least on the first energy-based target and the first audio feature measurement values, gain values of the audio frames; and after optimizing the gain values, applying the gain values to the first version of sound mix to obtain a second version of the sound mix.

Optimization for technical targets in audio content

Some implementations of the disclosure relate to a method, comprising: obtaining, at a computing device, first video clip data including multiple sequential video frames, the multiple sequential video frames including at least a first video frame and a second video frame that occurs after the first video frame; inputting, at the computing device, the first video clip data into at least one trained model that automatically predicts, based on at least features of the first video frame and features of the second video frame, sound effect data corresponding to the second video frame; and determining, at the computing device, based on the sound effect data predicted for the second video frame, a first sound effect file corresponding to the second video frame.

AI-assisted sound effect editorial

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.

System for deliverables versioning in audio mastering

Implementations of the disclosure describe systems and methods that leverage machine learning to automate the process of creating music and effects mixes from original sound mixes including domestic dialogue. In some implementations, a method includes: receiving a sound mix including human dialogue; extracting metadata from the sound mix, where the extracted metadata categorizes the sound mix; extracting content feature data from the sound mix, the extracted content feature data including an identification of the human dialogue and instances or times the human dialogue occurs within the sound mix; automatically calculating, with a trained model, content feature data of a music and effects (M&E) sound mix using at least the extracted metadata and the extracted content feature data of the sound mix; and deriving the M&E sound mix using at least the calculated content feature data.

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San Anselmo, CA, United States of America

Scott Levine