Lyrics for success: comparing stylometric and embedding features for song popularity prediction

This is the code that was submitted together with the paper at ISMIR 2024.

Set Up a virtual environment

We strongly advise to set up a virtual experiments for these experiments.

pip install -r requirements.txt

You will also need to download additional resources from nltk in Python in your virtual environment.

nltk.download('stopwords')
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')
nltk.download('vader_lexicon')

Reproducibility

For more clarity, we describe the different scripts to run to reproduce our experiments in a separate README.

Structure

TO-DO-G: add the notebook in src/models

Below an overview of the main content of the code, that is in the src folder:

• configs: configuration .yaml files for the regression layers
• data_prep: all scripts related to data preparation for model training
• models: all models
• embeddings.py: extract embeddings from a model
• features.py: stylometric features
• helpers.py: generic helpers