Music Score Classifier

This repository is the model trainer part of the Mobile Music Score Classifier, which is a mobile Android application that takes the live camera-feed and classifies the image in real-time into either music scores, or something else and displays the result in the application. It is part of a set of three tools:

Model Trainer	Mobile App	Manual Classifier
Trains a deep network to automatically classify images into scores or something else.	Mobile Android application that uses a trained model to perform real-time classification on a mobile device.	A small C#/WPF application that can be used manually classify images, used during evaluation

You might also be interested to check out my follow-up work.

Running the application

This repository contains several scripts that can be used independently of each other. Before running them, make sure that you have the necessary requirements installed. Note that there are two versions, depending on the Deep Learning framework you prefer: Tensorflow/Keras or PyTorch.

Requirements

This application has been tested with the following versions, but older and newer versions of Tensorflow and Keras are very likely to work exactly the same:

• Python 3.6
• Keras 2.2.4
• Tensorflow 1.11.0 (or optionally tensorflow-gpu 1.11.0)

or

• Python 3.6
• PyTorch 1.0

Optional: If you want to print the graph of the model being trained, install GraphViz for Windows via and add /bin to the PATH or run sudo apt-get install graphviz on Ubuntu (see keras-team/keras#3210)

We recommend Anaconda or Miniconda as Python distribution (we did so for preparing Travis-CI and it worked). To accelerate training even further, you can make use of your GPU, by installing tensorflow-gpu instead of tensorflow via pip (note that you can only have one of them) and the required Nvidia drivers.

Training the model

python TrainModel.py can be used to training the convolutional neural network. It will automatically download and prepare three separate datasets for training with Keras and Tensorflow (MUSCIMA dataset of handwritten music scores, Pascal VOC dataset of general purpose images and an additional dataset that was created for this project, containing 1000 realistic score images and 1000 images of other documents and objects).

The result of this training is a .h5 (e.g. mobilenetv2.h5) file that contains the trained model.

Troubleshooting: If for some reason the download of any of the datasets fails, stop the script, remove the partially downloaded file and restart the script.

Using a trained model for inference

You can download a trained model from here.

To classify an image, you can use the TestModel.py script and call it like this: python TextModel.py -c mobilenetv2.h5 -i image_to_classify.jpg

Exporting the Model for being used in Tensorflow

Since the Android App only uses Tensorflow, the resulting Keras model (despite having a tensorflow model inside) has to be exported into a Protobuf file. This is a bit cumbersome, because Tensorflow separates between the model description and the actual weights. To get both of them into one file, one has to freeze the model.

python ExportModelToTensorflow.py --path_to_trained_keras_model vgg.h5 will take the file vgg.h5 and create a file called output_graph.pb that is ready to be used in the Android application.

Additional Dataset

If you are just interested in the additional dataset that was created for this project, it can be downloaded from here. If you are using this dataset or the code from this repository, please consider citing the following publication:

@InProceedings{Pacha2017a,
  author    = {Pacha, Alexander and Eidenberger, Horst},
  title     = {Towards Self-Learning Optical Music Recognition},
  booktitle = {2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  year      = {2017},
  pages     = {795--800},
  doi       = {10.1109/ICMLA.2017.00-60},
}

License

Published under MIT License,

Copyright (c) 2019 Alexander Pacha, TU Wien

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.