Personalized music automatic generation system and method fused with multi-mode emotion perception

By integrating multimodal perception data and historical preference data, an emotional state vector and a music preference vector are constructed to generate a personalized music recommendation table. This solves the problem of difficulty in capturing users' real-time emotional state in existing technologies, and improves the accuracy and timeliness of personalized music recommendations.

CN122153113APending Publication Date: 2026-06-05SHENZHEN XUANTONG INFORMATION TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610260421.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-04
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing personalized music recommendation systems struggle to capture and respond to users' real-time changing emotional states, resulting in a discrepancy between the recommended results and the user's current emotional needs, and failing to achieve real-time, accurate matching driven by emotions.

Method used

By collecting multimodal perception data (physiological signals, facial signals, and interaction signals), cross-modal emotion extraction is performed. Combined with users' historical music preference data, emotional state vectors and music preference vectors are constructed, and a personalized music recommendation table is generated using music matching thresholds.

Benefits of technology

It achieves real-time dynamic adaptation driven by emotions, improving the accuracy and timeliness of personalized music recommendations and ensuring that the recommended content is highly consistent with the user's real-time emotional state and long-term preferences.

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Abstract

The invention provides a personalized music automatic generation system and method fusing multi-modal emotion perception, and relates to the technical field of personalized music automatic generation. Multi-modal perception data of a target user is collected, and emotion cross-modal extraction is performed on the multi-modal perception data to obtain an emotion state vector; determining a music preference vector based on the historical music preference data, and performing expected tendency on the music preference of the target user according to the emotional state vector and the music preference vector to obtain each music expected value of the target user in the current emotional state; predicting a currently expected music type of the target user according to each music expected value and a music matching threshold value to obtain music responsivity of the target user to each music type; and generating a personalized music recommendation table according to each music responsivity. According to the method, the current emotional state and the historical preference data of the user can be fused, real-time dynamic adaptation of emotion driving is realized, and the accuracy and timeliness of personalized music recommendation are improved.
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