Engineeering & IT Projects and Resources: AI Music Composer

AI Music Composer

1. Introduction

The AI Music Composer project aims to generate simple musical compositions using either Markov Chains or Long Short-Term Memory (LSTM) networks. These methods allow the system to create sequences of musical notes based on probabilistic or learned patterns.

2. Prerequisites

• Python: Install Python 3.x from the official Python website.
• Required Libraries:
- numpy: Install using pip install numpy
- tensorflow or keras: Install using pip install tensorflow
- music21: Install using pip install music21
• Basic knowledge of Python programming, Markov Chains, and LSTMs.

3. Project Setup

1. Create a Project Directory:

- Name your project folder, e.g., `AI_Music_Composer`.
- Inside this folder, create Python scripts for Markov Chain (`markov_music.py`) or LSTM (`lstm_music.py`).

2. Install Required Libraries:

Ensure numpy, tensorflow/keras, and music21 are installed using `pip`.

4. Writing the Code

Below is an example of music composition using Markov Chains:

import numpy as np
from music21 import stream, note, midi

# Define a simple Markov Chain
def create_markov_chain(notes):
    transitions = {}
    for i in range(len(notes) - 1):
        current_note = notes[i]
        next_note = notes[i + 1]
        if current_note not in transitions:
            transitions[current_note] = []
        transitions[current_note].append(next_note)
    return transitions

# Generate a sequence of notes using the Markov Chain
def generate_music(chain, start_note, length=50):
    generated_notes = [start_note]
    for _ in range(length - 1):
        current_note = generated_notes[-1]
        if current_note in chain:
            next_note = np.random.choice(chain[current_note])
            generated_notes.append(next_note)
        else:
            break
    return generated_notes

# Save the generated music to a MIDI file
def save_to_midi(notes, file_name):
    midi_stream = stream.Stream()
    for n in notes:
        midi_stream.append(note.Note(n))
    mf = midi.translate.music21ObjectToMidiFile(midi_stream)
    mf.open(file_name, 'wb')
    mf.write()
    mf.close()

# Example usage
if __name__ == "__main__":
    sample_notes = ['C4', 'E4', 'G4', 'C5', 'G4', 'E4', 'C4'] # Sample melody
    markov_chain = create_markov_chain(sample_notes)
    generated_notes = generate_music(markov_chain, start_note='C4', length=30)
    print("Generated Notes:", generated_notes)
    save_to_midi(generated_notes, 'generated_music.mid')

For LSTM-based music generation, refer to the following structure:

# Import necessary libraries
from keras.models import Sequential
from keras.layers import LSTM, Dense, Activation
import numpy as np

# Prepare data, build LSTM model, and train
# Generate sequences and save them as MIDI files

5. Key Components

• Markov Chains: Simple probabilistic model for note sequences.
• LSTMs: Recurrent Neural Networks for sequence learning.
• MIDI Generation: Use music21 to save sequences as MIDI files.

6. Testing

1. Run the Markov Chain script or train the LSTM model.

2. Verify the generated MIDI file by playing it using a media player.

7. Enhancements

• Harmonization: Add chord progressions to enhance melodies.
• Style Adaptation: Train the model on specific music genres.
• Real-Time Composition: Enable real-time music generation with user input.

8. Troubleshooting

• Poor Melody: Experiment with the Markov Chain transitions or retrain the LSTM model.
• MIDI Issues: Ensure correct installation of music21 and verify MIDI file format.
• Training Errors: Check dataset preparation and model configurations for LSTM.

9. Conclusion

This project demonstrates the basics of AI-driven music composition using Markov Chains and LSTMs. With additional training data and model refinement, it can produce complex and stylistic melodies.

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