Code base for reproducing the results presented in

J. Zeitler, S. Deniffel, M. Krause, and M. Müller. "Stabilizing Training with Soft Dynamic Time Warping: A Case Study for Pitch Class Estimation with Weakly Aligned Targets", in Proc. of the 24th Int. Society for Music Information Retrieval Conf., Milan, Italy, 2023.

© Johannes Zeitler ([email protected]), 2023

• ./Code/: contains all code for running the experiments

• ./Data/: Location for Schubert Winterreise Dataset and computed HCQTs

• ./Logs/: Training log files

• ./Models/: All trained models from the paper

• download the Schubert Winterreise Dataset (SWD) from Zenodo and store it in ./Data/
• install the conda environment (conda env create -f environment.yml)
• run ./Code/01_Preprocessing.ipynb to compute HCQTs for the SWD recordings
• run ./Code/02_Training.ipynb to train deep chroma estimators with SDTW loss
• run ./Code/03_Evaluation.ipynb to compute F-measures of trained models (= reproduce the results from Table 2 in the paper)
• run ./Code/04_SoftAlignmentMatrix.ipynb to compute the soft alignment matrices (= reproduce Figure 4 in the paper)

This work was supported by the German Research Foundation (DFG MU 2686/7-2). The authors are with the International Audio Laboratories Erlangen, a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institute for Integrated Circuits IIS.

This repository builts upon code from christofw/multipitch_mctc and Maghoumi/pytorch-softdtw-cuda.