This is a written project with regard to processing sound using keras deep learning library. These are the planned application:

• Genres classification
• Music auto-tagging
• Instrumental classification
• Tempo classification
• Deep Learning Jazz generator
• Sort music by genres, artist, tempo, tags
• Piano analysis

• Researching on processing Midi file format: tracks, messages, channels, etc.

• More research on processing Midi file format: tracks, messages, channels, etc.

• More research on MIDI file processing and feature engineering.
• Add data generator.
• Add code to read and parse midi file, converting them to structrured form.
• Add helper and utilization function.

• Edit reference for paper
• Add critical data preprocessing function: convert array of NoteEvent to a 2D array with shape (max_tick, note_range) where x axis is pitch and y axis is time.
• Update compressed dataset

• Finish Data Preprocessor and Data Generator. Working on building Convolutional Neural Network with analytic pixel. The paper can be found here
• Try building model with generative adversarial neural network.

• Build model with PixelCNN++ philosophy.
• Build model with Variational Autoencoder philosophy.
• Train 16000 epochs on CNN model, with dataset of single-track midi files.
• Log file surpassing (edit .gitignore)

• Switch from CNN model to seg-to-seg LSTM model. Do more research.

• Split the main notebook into one with music generator processing as image processing, the other as sequence processing (CNN and LSTM).
• Utilize a unique class for a note event for efficient data pre-processing.
• Add function to build note matrix from midi tracks.

• Succesfully train on Bach's prelude and fuge in C major BWV 846 piece, 2000 epoches, with accuracy of 89% and loss 12%.