Introduction to AI-Powered Music Composition

By Bill Sharlow

Day 1: Building an AI-Powered Music Composer

Welcome to the first day of our exciting journey into building an AI-powered music composer! In this series, we’ll explore the fascinating intersection of artificial intelligence and music composition, guiding you through the process of creating your own AI-generated music.

Understanding AI-Powered Music Composition

AI-powered music composition harnesses the capabilities of artificial intelligence to generate original musical compositions autonomously. By analyzing patterns and structures in existing music data, AI algorithms can learn to generate new melodies, harmonies, and rhythms that mimic the style of human composers.

Why AI in Music Composition?

AI-driven music composition offers several compelling advantages:

  1. Creativity Enhancement: AI can generate an endless stream of novel musical ideas, inspiring composers and expanding creative horizons.
  2. Exploration of Styles: AI algorithms can emulate various musical styles and genres, allowing composers to explore new territories and experiment with different aesthetics.
  3. Productivity Boost: AI-powered tools can assist composers in generating musical sketches and ideas more efficiently, speeding up the composition process.
  4. Collaborative Potential: AI can serve as a collaborative partner for composers, offering suggestions and generating musical material that can be further refined and developed.

Getting Started with AI-Powered Music Composition

To begin our journey, let’s familiarize ourselves with some fundamental concepts and tools:

  1. MIDI Format: Musical Instrument Digital Interface (MIDI) is a standard protocol for representing musical information in digital form. MIDI files contain data such as note pitch, duration, and velocity, making them ideal for machine learning-based music generation.
  2. Recurrent Neural Networks (RNNs): Recurrent neural networks are a class of artificial neural networks designed to process sequential data. RNNs are well-suited for music generation tasks due to their ability to capture temporal dependencies in musical sequences.

Example Code: MIDI Data Preprocessing

To demonstrate the preprocessing of MIDI data for training our AI model, let’s consider the following Python code snippet:

import mido
import numpy as np

def process_midi_file(file_path):
    midi_file = mido.MidiFile(file_path)
    notes = []

    for msg in midi_file:
        if msg.type == 'note_on':

    return np.array(notes)

# Example usage
file_path = 'path/to/your/midi/file.mid'
midi_data = process_midi_file(file_path)
print("Processed MIDI data:", midi_data)

In this code snippet, we use the mido library to read MIDI data from a file and extract note-on events. We then convert the extracted notes into a numpy array for further processing.


In this introductory blog post, we’ve explored the fascinating world of AI-powered music composition, discussing its potential benefits and key concepts. We’ve also provided a glimpse into the process of preprocessing MIDI data, a crucial step in training AI models for music generation.

In the next blog post, we’ll delve deeper into the process of collecting and preprocessing music data, laying the groundwork for training our AI music composer. Stay tuned for more exciting discoveries in the realm of AI and music!

If you have any questions or thoughts, feel free to share them in the comments section below!

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