pitchclass_tonalstructures

This is a pytorch code repository accompanying the following paper:

Christof Weiß and Meinard Müller From Music Scores to Audio Recordings: Deep Pitch-Class Representations for Measuring Tonal Structures
ACM Journal on Computing and Cultural Heritage, 2024

This repository only contains exemplary code and pre-trained models for some of the paper's experiments as well as some individual examples. All datasets used in the paper are publicly available (at least partially), especially our main dataset:

• Wagner Ring Dataset (WRD)

For details and references, please see the paper.

Feature extraction and prediction (Jupyter notebooks)

In this top folder, three Jupyter notebooks demonstrate how to

• preprocess audio files for running our models (01_precompute_features),
• load a pretrained model for predicting pitches (02_predict_with_pretrained_model),
• generate the visualizations of the paper's Figure 5 (03_visualize_pitch_class_features).

Experiments from the paper (Python scripts)

coming soon...

Run scripts using e.g. the following commands:
conda activate pitchclass_mctc
export CUDA_VISIBLE_DEVICES=1
python experiments/exp136b_traintest_schubert_sctcthreecomp_pitchclass.py

Application: Visualization (Figure 5)

• Please see the Jupyter Notebook 03_visualize_pitch_class_features.