Case study / 08 · Deep Learning

AI Music Generation

Exploring how sequential models learn patterns in symbolic music.

Deep Learning
UC San Diego · Course project
LSTMSequence ModelingMIDIPythonSymbolic Music

The idea

Instead of generating raw audio, this project explored symbolic composition: learning musical event patterns and producing MIDI sequences that can be played back with virtual instruments.

The experiment

01
Represent music as sequences

Used a notebook-based workflow to examine musical patterns and prepare model inputs for sequential generation.

02
Generate with and without conditioning

Produced two MIDI artifacts: an unconditioned sample and a conditioned sample.

03
Review examples

Created a separate project demonstration video showing the work in context.

What the artifacts show

The uploaded materials include two generated MIDI files. These demonstrate model outputs, but audio quality and model-comparison claims are subjective unless supported by a controlled evaluation.

Unconditioned MIDI ↗Conditioned MIDI ↗

See the demonstration

Watch the project video ↗

Explore the work

Read the original project materials behind this case study.