Project Title : AI Music Composer

Objective:
To build an AI system that can automatically generate music—melodies, chords, or full compositions—by learning patterns from existing music using machine learning or deep learning models.

What It Does:
The system learns musical structure (notes, rhythm, harmony) from a dataset and creates new pieces that sound human-composed.

Key Concepts:

Sequence modeling (music is a time-based sequence, like language).

Generative models like LSTM, Transformer, or GANs.

Representing music digitally (MIDI format, notes, durations, etc.).

Steps Involved:

Dataset Collection:

Use MIDI datasets like MAESTRO, Nottingham, Lakh MIDI, or any public domain music files.

Preprocessing:

Convert MIDI files into sequences of notes/events.

Normalize note durations, keys, and time signatures.

Encode notes into numerical formats (e.g., one-hot encoding or tokenized).

Model Building:

Use RNNs (LSTM/GRU) for melody generation.

Advanced: Use Transformers (like GPT) for better long-term memory.

Train model on note sequences to predict the next note(s).

Music Generation:

Seed the model with a short melody or starting note.

Generate a sequence of notes which can be converted back to MIDI/audio.

Output Conversion:

Convert generated notes to MIDI files.

Use a MIDI player or synthesizer to play the music.

Applications:

Background music generation for games, apps, or videos.

Tools for musicians and composers.

Personalized music creation.

Creative AI in entertainment.

Tools & Technologies:

Languages: Python

Libraries: Music21, pretty_midi, TensorFlow/Keras, PyTorch, Magenta (by Google)