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README

Project Overview

This repository contains data and scripts for training supervised classifiers (e.g., neural networks) on labeled acoustic datasets. The project is organized into three subsets:

  • Subset_A/
  • Subset_B/
  • Subset_C/

Each subset has the following folder structure:

  • 1_Original_Data/

    • original_data_for_classification.csv: Main dataset containing labels and extracted features required for building a supervised classifier.
    • Labeled_Data_With_Features.xlsx: Source file used to generate original_data_for_classification.csv. This file should not be modified.

  • 2_Clean_Data/

    • Labeled_Data_to_Review.xlsx: File used to manually review and filter the dataset.
    • cleaned_data_for_classification.csv: Cleaned version of the dataset. This is the dataset to be used for training the second set of models.

The repository includes a Jupyter notebook that was used to create both original_data_for_classification.csv and cleaned_data_for_classification.csv from the source files (Labeled_Data_With_Features.xlsx and Labeled_Data_to_Review.xlsx).

The training and evaluation pipeline implemented is contained within train_and_eval.ipynb. SVM models are tuned individually for both versions of every subset. Metrics reported include:

  • F1 Score: Overall and per-fold.
  • Accuracy: Overall and per-fold.
  • Final Confusion Matrix
  • Statistical Testing: A Wilcoxon signed-rank test on F1 scores across folds is used to determine if the improvement is statistically significant.
  • Visual Summary: Boxplots compare overall F1-Score and Accuracy performance across both versions of all subsets.