Meme-to-Audio Synthesis

Developed a pioneering cross-modal system that synthesizes contextually relevant audio based on the deep semantic reasoning of internet memes.

• The "Humor Gap" Challenge: Solved the limitation where current LLMs/Vision models struggle to decode the complex, non-linear humorous logic (irony, dark humor) that arises from the "semantic friction" between text and image.

• Advanced Reasoning: Leveraged cutting-edge Multimodal Alignment (e.g., SigLIP/BLIP-2) and Foundation Models (e.g., LLaVA/Gemini Vision) to interpret hidden humorous metaphors.

• PM Impact - Capability for Virality: Lowered the content creation barrier by enabling models to autonomously identify "meme-able" visual patterns and pair them with emotive, context-aware audio, fostering a richer ecosystem for shareable content.