This repository contains an example for multiclass classification as introduction to machine learning on audio signals. Different bulk materials create characteristic structure borne sounds when rolling/slipping down a ramp. Audio recordings from different types of screws and bolts rolling down an aluminium ramp have been conducted. A deep neural network (DNN) is trained and evaluated for the classification of the type of bulk metarial rolling down the ramp.
- Install a Python 3.7 environment on your computer, e.g. Anaconda, including support for jupyter notebooks
- Check if the following Python packages are installed
- numpy
- matplotlib
- pysoundfile
- tensorflow
- keras
- scikit-learn
- Clone the repository
- Open the jupyter notebook train_model.ipynb and run all cells
- Take a look at the exercises at the end of the notebook
The notebooks are provided as Open Educational Resources. Feel free to use the notebooks for your own purposes. The text/images/data are licensed under Creative Commons Attribution 4.0, the code of the IPython examples under the MIT license.