Jazz Music Generation with GPT and GAN
This project explores two different approaches to jazz music generation using deep learning: GPT-based language models and Generative Adversarial Networks (GANs). The project works with jazz MIDI files to train models that can generate new jazz compositions. GPT models treat music generation as a sequence-to-sequence problem with attention mechanisms for long-term dependencies, while GANs use adversarial training to learn the distribution of jazz music patterns. The project demonstrates both approaches, compares their effectiveness in generating musically coherent jazz pieces, and includes comprehensive MIDI data processing, model training, and evaluation.
Overview
This project explores two different approaches to jazz music generation using deep learning: GPT-based language models and Generative Adversarial Networks (GANs). The project works with jazz MIDI files to train models that can generate new jazz compositions. GPT models treat music generation as a sequence-to-sequence problem with attention mechanisms for long-term dependencies, while GANs use adversarial training to learn the distribution of jazz music patterns. The project demonstrates both approaches, compares their effectiveness in generating musically coherent jazz pieces, and includes comprehensive MIDI data processing, model training, and evaluation.
Key Features
GPT-based sequence-to-sequence jazz music generation
GAN-based adversarial jazz music generation
MIDI data preprocessing and encoding
Transformer architecture with attention mechanisms
Adversarial training with Generator-Discriminator
Jazz-specific music pattern learning
Polyphonic music handling
Model training and evaluation
Generated music output in MIDI format
Comparison of GPT vs. GAN approaches
pages.portfolio.projects.jazz_music_generation_gpt_gan.features.10
Technical Highlights
Implemented GPT-based transformer model for jazz music generation
Developed GAN architecture for adversarial music generation
Processed jazz MIDI dataset for training
Compared two different deep learning approaches
Generated musically coherent jazz compositions
Handled polyphonic music with multiple simultaneous notes
Challenges and Solutions
Music Representation
Converted MIDI to model-friendly format using event-based encoding and piano roll representation
Long Sequences
Handled long jazz pieces using sequence chunking, attention mechanisms, and hierarchical models
Musical Coherence
Maintained musical structure through training on structured data, conditioning on musical features, and post-processing
GAN Training Stability
Applied WGAN-GP, spectral normalization, and progressive training techniques for stable GAN training
Evaluation Metrics
Developed multiple metrics for harmonic, rhythmic, and melodic quality assessment
Style Preservation
Maintained jazz characteristics through style conditioning, jazz-specific training data, and feature constraints
Technologies
Deep Learning
Models
Music Processing
Data
Environment
Project Information
- Status
- Completed
- Year
- 2025
- Architecture
- Dual-Model Music Generation with GPT and GAN Approaches
- Category
- Data Science