Real-Time Live-Stream Augmentation for Audio Customization
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Solution Overview
Problem
Live streaming often results in static and homogenous content, making it difficult for presenters to engage diverse audiences and manage interactions effectively, leading to reduced engagement and increased resource consumption.
Innovation Solution
The implementation of real-time live-stream augmentation (RLA) using multimodal and audio machine learning models to generate customized, dynamic audio streams based on both presenter and audience content features, allowing for personalized audio signals to be added or modified in real-time, reducing the need for multiple stream versions and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple stream versions are prepared to engage diverse audiences, then audience engagement is improved, but network bandwidth and storage costs increase
Solution Approach 1:
The patent segments the audio stream into base audio and customizable audio components. The base audio stream is transmitted once to all viewers, while individual customization elements (sound effects, music, commentary) are separated and added on-demand based on user preferences, eliminating the need to store and transmit multiple complete stream versions.
Solution Approach 2:
The system dynamically generates customized audio streams in real-time based on viewer profiles and stream content. Instead of pre-preparing multiple static stream versions, the audio customization module dynamically selects and combines audio elements based on individual viewer preferences, enabling adaptability without proportional increases in bandwidth and storage.
2Adaptability or versatility
If real-time audio customization is implemented for each viewer, then audience engagement is improved, but processing complexity increases
Solution Approach 1:
The processing system is segmented into distinct functional modules: stream content analysis module, viewer profile analysis module, and audio customization module. Each module handles specific tasks independently, making the overall complex process more manageable and scalable. The audio customization module only processes audio elements rather than the entire stream, reducing computational burden.
Solution Approach 2:
Instead of processing the entire audio stream for every viewer, the system applies audio customization only to relevant portions (sound effects, music, commentary) based on viewer profiles. This partial action approach customizes only what is necessary for each viewer while avoiding redundant processing of the entire stream.
3Loss of energy
If static homogenous streams are used, then resource consumption is reduced, but audience engagement decreases
Solution Approach 1:
The system transitions from static homogenous streams to dynamic personalized streams. A single base audio stream is enhanced with dynamic audio customizations based on real-time viewer profiles and preferences. This dynamic approach maintains resource efficiency by using one base stream while adding only necessary customization elements, rather than creating multiple static versions.
Solution Approach 2:
Instead of creating entirely different streams for different audiences, the system applies local quality customizations to specific portions of the audio stream based on individual viewer preferences. Each viewer receives the same base quality with locally tailored additions (sound effects, music, commentary), optimizing engagement without requiring complete stream redundancy.
Data Source
AI summary
A live stream, that includes a video stream and an audio stream, of a presenter is monitored. The live stream is attended by an audience that includes one or more audience members. One or more stream content features of the live stream at a first window of time is transmitted to a multimodal machine learning model. One or more audience content features of the audience at the first window of time is transferred to the multimodal model. One or more feature results, based on the stream content features and based on the audience content features, of the first window of time is obtained from the multimodal model. The feature results are sent to an auditory machine learning model. A first audio signal from the auditory machine learning model is received. An augmented stream of the first window of time is generated based on the first audio signal.