Context-Based Audio Generation From Multimodal Content Cues
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Solution Overview
Problem
The process of sorting through and sharing music and other audio content associated with social media content is time-consuming and complex, requiring manual effort to find relevant and contextually appropriate audio segments.
Innovation Solution
A computing system uses machine-learned models to detect, recognize, and classify features in multimodal data such as images, text, and video, generating context-based audio content by determining contexts and selecting or generating audio segments based on these features, which can be shared as link notes with other users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to sort through and select audio content, then users can carefully choose contextually appropriate music, but the process becomes time-consuming and complex
Solution Approach 1:
The system performs automatic audio content selection and contextual analysis without requiring manual user intervention. The machine-learned models independently analyze content features, determine appropriate contexts, and generate audio segment recommendations, allowing the system to serve itself rather than requiring user effort for each selection task.
Solution Approach 2:
The patent replaces manual mechanical sorting and selection processes with automated machine-learned models. These models use feature detection and contextual analysis to automatically identify and select appropriate audio segments, substituting human cognitive and manual efforts with computational algorithms that process content metadata, audio characteristics, and contextual information.
2Productivity
If automated machine-learned models are used to generate audio content, then the process becomes efficient and fast, but system complexity increases
Solution Approach 1:
The system divides the audio content generation task into distinct functional segments: feature detection module, contextual analysis module, audio segment selection module, and content generation module. Each module performs a specific function and can be independently optimized or replaced, reducing overall system complexity while maintaining high productivity through specialized processing at each stage.
Solution Approach 2:
The machine-learned models are designed to perform multiple functions: they detect various content features, analyze different types of contextual information, select appropriate audio segments, and generate diverse content formats. This multi-functionality reduces the need for separate specialized systems, managing complexity while maintaining high productivity across different content generation tasks.
Data Source
AI summary
Methods, systems, devices, and non-transitory computer readable media for generating context-based audio content are provided. The disclosed technology can include receiving content data comprising content associated with one or more data multimodalities. One or more prompts associated with the content can be received. One or more contexts associated with the content data can be determined. Based on inputting the content data, the one or more prompts, and context data based on the one or more contexts into one or more machine-learned models, one or more context-based audio segments based on the content data can be generated. The one or more machine-learned models can be configured to generate the one or more context-based audio segments based on recognition of one or more features of the content data and the context data. Furthermore, context-based audio content based on the one or more context-based audio segments can be generated.