kitchen soundscapes

Autoencoder-generated, neural network music based off my musical opinion of objects in my kitchen

Metis data-science bootcamp passion project, Mar 01 - Mar 25 2021

See the final product

See the blog post

Project was presented, slides!

Summary: Website view of a neural network's generation of music inspired by my own music about kitchen objects. The approach is to see music as an image, not a text. Input MIDI files are extracted for notes/chords, and an autoencoder is trained on that information.

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Modules used:

• music21
• jupyter notebook
• autoencoder
• lstm
• other modules: scikit-learn numpy

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The data:

• The dataset can be found in the "music" folder

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The process:

• The basic idea is to use load_songs.py (or get_songs as is in the jupyter notebooks) to retrive notes/chords
• We then encode that using one-hot encoding (in the case of basic_autoencoder.py and lstm.py) or encode that into a sparse matrix as is in autoencoder_with_sparse_matrix.py
• We train an autoencoder/lstm on that data and generate a note based off a random starting point
• We can decode that back into notes and download it as a .mid file

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On the web:

• The project can be found here