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Bytehoven - Sheet music recognition with ResNet50


This repository contains the resources used in the development of Bytehoven, a deep learning sheet music recognition model currently in development. Please find the full tutorial here. The current version recognises piano music from 45 composers (see 'composerlist.csv').

Trained model weights are available under 'releases'. We'll keep training on more data and composers to increase Bytehoven's knowledge base.


Dataset organisation and methods

Source material has been downloaded from the Petrucci Music Library. Datasets have been generated by downloading all piano works from each composer, and splitting each piece into images of individual rows of music. For de Dev and Test set, full compositions were taken out of the training set, to prevent the net from recognising thematic works or repeated phrases. The main point of interest was whether the net could generalize a composer's style to unseen works.

The net was trained for 100 epochs, starting with learning rate 0.001 which decayed linearly to 0 over epochs.

Current Results

The current released model version (V0.4) scores 73.53% validation accuracy on the test set (99.97% train set, 71.30% dev set), and can discriminate between a total of 45 composers.

When evaluating difficult to classify composers, an interesting pattern arose with Rubinstein. Anton Rubinstein is often confused with Tchaikovsky and Rachmaninov:


This is especially interesting, since Rubinstein was Tchaikovsky's composition teacher, and Rachmaninov's early compositional influence was Tchaikovsky (he also expressed having Rubinstein as a role model).

Contemporaries Debussy and Saint-Saens are also confused by the model. Interestingly, these contemporary composers were not fans of each other's work. Immortalised in the quotes:

Saint-Saens: "I have stayed in Paris to speak ill of Pelléas et Mélisande." (a Debussy opera)

Debussy: "I have a horror of sentimentality, and I cannot forget that its name is Saint-Saëns." source


It seems these contemporary rivals may share more stylistic devices that they might have liked to admit, at least in the eyes of Bytehoven.

Lastly, the most impossible composer to classify is Clara Schumann:

Clara Schumann

What is so difficult about classifying her work eludes me. She is often confused with Chaminade (the only other female composer in the set!) and Satie. Both were contemporaries of Clara in her later years, so there might have been some influence there.

Included files

  • datasets/Musicdata_Small.rar -- Dataset of small sized images (200*35px)
  • datasets/Musicdata_Medium.rar -- Dataset of medium sized images (400*70px)
  • -- ResNet50 architecture implemented in Keras
  • -- Example to initiate training run


  • Release V0.1: bytehoven-7-weights.hdf5 -- Model weights trained on medium sized set with 7 composers (full training log included), reaching 99.5% validation accuracy on test set.
  • Release V0.2: bytehoven-22-weights.hdf5 -- Model weights trained on 22 composers (training log and composer list included), reaching 76.44% validation on test set and 98.23% on the train set.
  • Release V0.4: bytehoven-45-weights.hdf5 -- Model weights trained on 45 composers (training log and composer list included).

Network activation and filter visualisation

Final network layer activation for two random pieces:

Beethoven: Beethoven

Chopin: Chopin

Random filters visualised (filters lower in the image are deeper in the network): Filters


Bytehoven - Sheet Music Recognition with ResNet50 Deep Network




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