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# System prepended metadata

title: GABE Project Report Template

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# GABE Project Report Template


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## Preliminary MK/MW Notes

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## Project Description

### Project/Tool Overview
**Project Name:** Generative Music Model  
**Associated Harvard Course:** Music 225

**Summary:**  
Gabe is developing a generative model to create musical notes for Music 225, focusing on musical forms, especially in classical music. The project aims to explore the capabilities of AI in understanding and generating musical forms. Utilizing TensorFlow and Keras, the model is designed to train on music data and generate new compositions, challenging the notion of machine comprehension of musical form.

**Technology Stack:** TensorFlow, Keras, Python, custom Python library for transformer models.

### Learning Objectives and Benefits
**Objectives:**  
- To explore the intersection of music and computer science through AI-generated compositions.
- To challenge and expand the understanding of musical forms through generative AI.

**Benefits:**  
Students and educators in Music 225 will benefit from a novel approach to understanding musical forms, fostering a deeper appreciation and comprehension of music theory and composition through the lens of AI technology.


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## Bi-Weekly Update
**Date:** 20240216

![alt text](https://files.slack.com/files-pri/T0HTW3H0V-F06LADYJNJZ/screen_recording_feb_23.gif?pub_secret=8f259931e6)

### Interesting Developments
Gabe's generative music model has now been trained on a broader dataset, including pieces from varied musical periods, showing promising results in generating compositions that maintain stylistic integrity.

### Resource Requests
Seeks access to a larger corpus of musical scores and potentially computational resources to handle more complex model training phases.

### Best Practices & Learnings
Highlighting the value of cross-disciplinary research, Gabe notes that insights from music theory have been crucial in refining the AI model's output to better align with human musical understanding.

## Collaboration and Feedback

### Staff Collaboration
Feedback from music theory professors and Chris has been instrumental in adjusting the model's parameters to produce more harmonically pleasing results.

### Feedback Implementation
focus on polyphonic structures

## Future Directions

### Next Steps
Plans to refine the model further with a focus on dynamic variation and expression, aiming to produce compositions that not only follow musical theory but also convey emotion.

### Long-Term Vision
Gabe hopes to explore the potential for AI in music education, particularly in composition and analysis, potentially revolutionizing how students interact with musical creativity.

## Additional Notes

### Miscellaneous Information


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## Bi-Weekly Update
**Date:** 20240301


