A tool that is able to match a given MusicXML query to the most similar piece in a database of classical music.
Follow the instructions on the MidiBERT GitHub page to download the MidiBERT model checkpoints. Save the pretrain_model.ckpt file in the BeMuse/artifact/midibert directory.
We provide a brief overview of the project setup and the steps used to analyze a MIDI input query. For clarity, a diagram of the model architecture is provided below:
BeMuse begins by preprocessing a MIDI input file into a compound word sequence (CP), which is passed as input to a fine-tuned MidiBERT encoder. The encoder outputs a 768-dimensional vector representation of the input in latent space. Then, the input is compared against a pre-computed database of song vectors using cosine similarity. After comparing the input vector against all possible outputs, the tool outputs the top 5 most-similar songs to the given input.