Music-Object-Classification

Introduction

Music Object Classification is a part of Optical Music Recognition (OMR) where we task the computer to filter out and detect music symbols or objects in music score sheet. In this project, I have simplified the problem down to classifying individual musicial objects. I gathered a small subset of the DeepScores dataset, which had collection of annotated labels for 118 musical objects. I will outline the steps below to recreate this project on your machine.

Set up project folder

Git clone the project or download and unzip in machine. This will be the local project folder.

Downloading the dataset

This project is based in the DeepScores dataset. DeepScores is a collection of datasets that musical scores in digitized format. It has datasets for both simple object classification and segmentation. We only need the dataset for the object classification for this problem. In the website for DeepScores, download the DeepScores-Classfication dataset along with class-names.csv. First, move the class-names.csv in the dataset folder, next move dataset folder into your project folder.

Setting up a virtual environment and dependencies

It is recommended to setup a virual environment. Follow steps below to do this.

First install python3 if not install already. Then run the bash command on terminal below to install virtual-env.

sudo apt install python3-venv 

Next, switch to your project folder, You can now create the custom virtual env with the following command

python3 -m venv my-project-env

Now, active the virtual environment with the following command.

source my-project-env/bin/activate 

Next install pip if not installed. Then execute the following command to install all dependencies for the project

pip install -r requirements.txt

Setting up the scripts.

Make sure to point the data_path variable to the path of the DeepScores-Classfication folder. The rest should work fine.

Testing

There are three main scripts in this project. dataset_reader.py processes the dataset cleaning training and testing. The first major script is detection.py. I have implemented a simple template matching using pearson correlation coefficients. I have set up the possible experiments that you can try with following commands.

For one-to-one matching with randomized templates on a on a batch_size of 1000 test samples execute the following:

 python detection.py --test_mode="one_to_one" --batch_size=1000 --randomized=False

For finding best match with randomized templates on a on a batch_size of 1000 test samples execute the following:

 python detection.py --test_mode="best_match" --batch_size=1000 --randomized=False

Set the flag bash --randomized to bash False if you want to test it out on averaged templates.

Next, I have implemented a simple Convolutional Neural Network (CNN) using the Keras framework. To train the model on the dataset, execute:

python model.py 

The parameters for training can be tweaked with the following lines in model.py.

# traning parameters 
batch_size = 10
num_classes = 118 
epochs = 100

Feel free to play around with them and train the network locally on your machine. Training the model producing, metrics both on the terminal and visually. To make predictions later on saved checkpoint files, execute:

python predict.py 

Reference

I have borrowed and modified code from the following references for this project

Keras Documentation example for CNN

DeepScores test repository