Towards Human-in-the-loop Onset Detection: A Transfer Learning Approach for Maracatu

Contents

• Towards Human-in-the-loop Onset Detection: A Transfer Learning Approach for Maracatu
  • Contents
  • Code
  • Installation
  • Setting Up the Conda Environment
  • How to Rerun the Full Experiment Locally
  • Additional Figures and Tables
  • License
  • Citation

Code

This directory contains Python scripts for the project. These scripts are provided for reproducibility purposes and are not executable without full environmental setup and data, e.g. base models and Maracatu dataset: - code/ - data/: Raw and processed datasets used in the study, organized by instrument types. Note: Maracatu dataset is available upon request to the authors as identified in the paper. - models/: Trained models ready for evaluation. Note: Base models (trained for onset detection TCNv1 or beat tracking TCNv2) are available upon request to the authors as identified in the paper. - outputs/: Output files from model runs, categorized by the musical tradition studied. - pkl/: Serialized Python objects related to the Maracatu datasets. - src/: Source code of the project. - modules/: Contains various Python modules for analysis, model definitions, and utilities. - exports/: Additional exportable resources or processed files.

Installation

Clone this repository to view all files locally.

Setting Up the Conda Environment

This project provides two Conda environment files: - environment.yml for macOS M1 users which includes specific versions for compatibility. - minimum_environment.yml for users on other platforms, including only the core libraries needed across all systems.

This project uses a Conda environment to ensure all dependencies are managed correctly. The provided environment.yml file is tailored for compatibility with macOS M1 systems, which have specific requirements for TensorFlow and Keras versions. Other users are advised to start with the minimum_environment.yml and make any necessary adjustments based on their specific system requirements.

For macOS M1 Users: bash conda env create -f environment.yml conda activate maracatu

For non-M1 Users: bash conda env create -f minimum_environment.yml conda activate maracatu_minimum

Note: This will install all required packages via Conda and pip as specified in the environment file. Please review the package versions and adjust them as necessary.

How to Rerun the Full Experiment Locally

1. Obtain Base Models

   Request the .h5 base model files for TCNv1 and TCNv2 from the corresponding authors (as identified in the paper), and place them in the /models folder.

2. Obtain Maracatu dataset

   Request the audio (*.flac) from dataset authors and place it in the /data/**/audio folder.

3. Run the Experiment Script

   bash python rerun_experiment.py This script:

   • Fine-tunes models for each of Maracatu subdatasets.
   • Updates the ledger with new run IDs.
   • Saves finetuned models in the /exports folder with respective run IDs.
   • Creates predictions for each baseline model (madmomRNN, madmomCNN) and saves them in the /exports folder.
   • Creates original and finetuned model predictions for each Maracatu subdataset and saves them in the /outputs folder.

Additional Figures and Tables

The docs folder is organized into two subdirectories to facilitate navigation and understanding

• docs/figures/, this folder contains boxplots (*.pdf) related to performance metrics like F-measure, Precision, and Recall:

  • Files are named following the convention: <typeofplot>_<metric>_tol_<tolerance>.<basemodel>.pdf
    • typeofplot: Type of the plot (e.g., boxplot)
    • metric: Performance metric (e.g., fmeasure, precision, recall)
    • tolerance: Tolerance levels indicating the timing precision required (e.g., 10ms, 15ms)
    • basemodel: The base model version used (e.g., TCNv1, TCNv2)
  • Examples:
    • boxplot_fmeasure_tol_10ms.TCNv1.pdf: Boxplot showing F-measure performance at 10ms tolerance using TCN version 1
    • boxplot_precision_tol_25ms.TCNv2.pdf: Boxplot showing Precision at 25ms tolerance using TCN version 2
    • Note: The figures whose names start with compact_ are the ones used in the paper.

  These documents are provided in high quality to enhance detailed visualization and understanding, not possible within the limited size constraints of the paper.

• docs/tables/, this folder contains full tables (*.pdf) detailing performance metrics such as F-measure, Precision, and Recall, along with counts of True Positives (TP), False Positives (FP), and False Negatives (FN) across the Maracatu dataset for baseline and finetuned models on the TCNv1 and TCNv2 network.

License

This project, including all code and figures, is licensed under the GNU General Public License v3.0. By using, distributing, or contributing to this project, you agree to abide by the terms of the GPL v3.0.

The full license text is included in the LICENSE file in this repository. Further details and FAQs about the GPL v3.0 can be found at https://www.gnu.org/licenses/gpl-3.0.html.

Citation

If you use this software or data in your research, please cite the following paper:

Pinto, A. S. (2025). Towards Human-in-the-Loop Onset Detection: A Transfer Learning Approach for Maracatu. Proceedings of the 26th International Society for Music Information Retrieval Conference (ISMIR), Daejeon, South Korea.

arXiv link | Zenodo DOI

You can also cite the repository directly by using the CITATION.cff file or clicking on the “Cite this repository” button on the right sidebar of this GitHub page.