A Basic Tutorial on Novelty and Activation Functions for Music Signal Processing

This repository contains Jupyter Notebooks that include Python source code and audio excerpts for reproducing and experimenting with the examples discussed in the tutorial:

Meinard Müller and Ching-Yu Chiu. A Basic Tutorial on Novelty and Activation Functions for Music Signal Processing. Transaction of the International Society for Music Information Retrieval (TISMIR), ??: ??–??, 2024.

You can establish a conda environment by employing the environment.yml file. This approach includes necessary packages like jupyter to facilitate running demo notebooks.

1. Ensure you have Conda installed on your computer, for example, by using Miniconda.
2. Then, run the following command:

conda env create -f environment.yml

To explore ENF notebooks:

1. Clone the repository: Download the edu_novfct repository to your local machine using the following git command:

git clone https://github.com/groupmm/edu_novfct.git

2. Install Jupyter: If not already installed via the conda environment setup, install Jupyter to run the notebooks:

pip install jupyter

3. Launch Jupyter Notebook: Start the Jupyter notebook server by executing:

jupyter notebook

This will open a browser window from where you can navigate to and open the example notebooks.

We are happy for suggestions and contributions. We would be grateful for either directly contacting us via email ([email protected]) or for creating an issue in our GitHub repository. Please do not submit a pull request without prior consultation with us.

The code for this repository is published under an MIT license. This does not apply to the data files:

• Audio files are taken from the FMP notebooks.

The International Audio Laboratories Erlangen are a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institute for Integrated Circuits IIS.