Make Your Favorite Music Curative: Music Style Transfer for Anxiety Reduction
Anxiety is the most common mental problem that affects nearly 300 million individuals worldwide. The situation is even worse recently. In clinical practice, music therapy has been used for more than forty years because of its effectiveness and few side effects in emotion regulation. In this project, we propose a novel style transfer model to generate the therapeutic music according to user’s preference.
Automatic Image Annotation via Deep Learning
This project works on a basic research problem in multimedia content analysis: automatic image annotation, which labels the semantic content of images with a set of keywords. The research on image annotation has great scientific merit because it directly addresses an ultimate problem in multimedia society that enables the computer to understand and represent the visual information like human.
What Striks the Strings of Your Heart?
- Multi-Label Dimensionality Reduction for Music Emotion Analysis
Music can convey and evoke powerful emotions. This amazing ability has fascinated the general public and also attracted the researchers from different fields to discover the relationship between music and emotion. Psychologists have indicated that some specific characters of rhythm, harmony, melody, and also their combinations can evoke certain kinds of emotions. Their hypotheses are based on real life experience and proved by psychological paradigms on human beings. Aiming at the same target, this project intends to design a systematic and quantitative framework, and answer three widely interested questions: 1) what are the intrinsic features embedded in music signal that essentially evoke human emotions; 2) to what extent these features influence human emotions; and 3) whether the findings from computational models are consistent with the existing research results from psychological experiments.
Deliver Effective Training of Clinical Communication
To deliver effective training of clinical communication, this project utilizes artificial intelligence techniques to provide clinical staffs customized and lifelike practices with timely feedback under various scenario. Three objectives are targeted: 1) a task-oriented multi-turn conversation system to practice communication; 2) a real-time automatic assessment; 3) a friendly platform for clinical staffs to design new training tasks by themselves.