Research Areas

The Rebecca Lin AI Lab conducts cutting-edge research across multiple domains of artificial intelligence.

🎵 Music & Audio AI

Our research in music AI spans several exciting areas:

  • Music Generation: Creating original music using neural networks, GANs, and transformer models
  • Music Classification: Genre detection and music information retrieval using deep learning
  • Semantic Understanding: Linking visual and auditory modalities for cross-modal music generation

Key Projects: BGT-G2G

🧠 Neural Networks & Deep Learning

We explore advanced neural architectures and training strategies:

  • Generative Adversarial Networks (GANs): Multi-class generation and domain adaptation
  • Recurrent Neural Networks: GRUs and LSTMs for sequential data modeling
  • Transformer Models: Attention mechanisms for music and multimodal understanding
  • Transfer Learning: Adapting pretrained models for specialized tasks

Key Projects: GAN Multi-Class Credit Score, GRU Classifying GTZAN

🎯 Fuzzy Logic Systems

Research in uncertainty handling and intelligent decision-making:

  • Fuzzy Inference Systems: Rule-based AI for uncertain environments
  • Fuzzy Control: Adaptive control systems using fuzzy logic
  • Neuro-Fuzzy Integration: Combining neural networks with fuzzy systems

👁️ Image Analysis & Computer Vision

Multimedia understanding and visual information processing:

  • Image Classification: Deep learning for visual recognition tasks
  • Cross-Modal Learning: Connecting visual and auditory information
  • Image-to-Music Translation: Generating music from visual inputs
  • Object Detection: Identifying and localizing objects in images

Key Projects: IARP (Image Analysis at FCU), BGT-G2G, Deepfake Detection: Cross Xception-ViT

🤖 Recommender Systems

Personalized content recommendation algorithms:

  • Collaborative Filtering: User-based and item-based recommendations
  • Content-Based Filtering: Feature-based recommendation systems
  • Hybrid Approaches: Combining multiple recommendation strategies
  • Evaluation Metrics: Novel approaches to measuring recommendation quality

Key Projects: Recommender System

Interested in collaborating or learning more? Contact us!