ML-Driven Audio Intelligence for TableTalk

Problem Statement In interactive tabletop environments, Game Masters (GMs) face significant cognitive load when managing audio assets during live narration. Manually searching for and triggering specific dramatic recordings often disrupts the immersive flow of the session. There is a need for an automated system that can understand narrative intent and retrieve assets without interrupting the storytelling process.Proposed Solution I plan to bridge the gap between raw audio data and narrative utility by developing an automated pipeline for the TableTalk ecosystem. Leveraging my background in Computer Science and Economics and experience in agentic AI frameworks, I will implement:Narrative Tone Classification: Using a machine learning framework to categorize voice recordings into distinct narrative states (e.g., Urgent, Calm, Fearful).Hybrid Semantic Retrieval: Creating a retrieval engine that combines structured filtering with semantic ranking to allow GMs to use natural-language queries.Real-Time Trigger Logic: Implementing stream-based keyword spotting to map a storyteller's live performance to environmental atmospheric effects like lighting and soundscapes.DeliverablesQuantized Inference Engine: A lightweight model optimized for on-device mobile execution to ensure low-latency narrative classification.Search & Retrieval Module: A high-fidelity interface for natural-language audio discovery.Atmospheric Bridge: A software component that translates identified narrative tones or keywords into external hardware triggers.Technical Documentation: A complete guide and implementation roadmap for integrating these modules into the TableTalk mobile application.This project combines technical machine learning rigor with a deep appreciation for narrative immersion, informed by my previous work in multi-genre game development and algorithmic optimization