Abstract

A system and method for passive longitudinal behavioral delta detection through multi-modal audio analysis is disclosed. The system comprises an edge audio capture device for continuously capturing ambient audio, an on-device multi-modal feature extraction pipeline extracting acoustic features as semantic-level representations, topic-level natural language embeddings without retaining specific words, behavioral graph topology interaction data, and classified non-verbal sound event frequency vectors, and a longitudinal delta detection engine computing temporal deltas at multiple resolutions against a historical baseline. A privacy architecture ensures on-device processing with content obliteration and hashed entity identity. A model adaptation layer provides supervised, unsupervised membership testing, and human-in-the-loop calibration paths sharing a common feature pipeline. An optional session alignment module enables narrative self-comparison over time. The system produces rich aggregate analytics including cognitive trajectories for elder care and team dynamics scorecards for business environments, functioning as a slow signal detector that identifies gradual behavioral changes over weeks, months, or seasons without manipulating monitored individual behavior.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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