Benchmarking machine learning for bowel sound pattern classification – from tabular features to pretrained models

This is the official code repository associated with the paper:

📄 Benchmarking machine learning for bowel sound pattern classification – from tabular features to pretrained models

✍️ Zahra Mansour, Verena Uslar, Dirk Weyhe, Danilo Hollosi and Nils Strodthoff. ArXiv. https://arxiv.org/abs/2502.15607

This repository contains a Bowel sounds classification pipeline that supports both Deep Learning and Machine Learning models for audio classification. The pipeline is built using PyTorch, Hugging Face Transformers, and Scikit-Learn.

Features

Supports multiple model architectures:

• Deep Learning Models: VGG, ResNet, AlexNet, CNN-LSTM
• Pre-trained models: Wav2Vec, HuBERT
• Machine Learning Models: SVM, XGBoost, KNN, Decision Tree (DTC), CatBoost

Automatic feature extraction:

• Extracts spectrogram, log-mel, MFCC, or raw waveform for deep learning.
• Supports GeMAPS & ComParE feature extraction for machine learning.

Stratified Data Splitting:

• Ensures class distribution remains balanced across train, validation, and test sets.

Configurable settings using config.yaml:

• Easily change dataset paths, model type, and hyperparameters without modifying the code.

Automatic Model Training & Evaluation:

• Computes accuracy, F1-score, confusion matrix, and AUC (Area Under Curve) for evaluation.

---

Usage

• Install dependencies

pip install torch torchvision torchaudio transformers pandas scikit-learn pyyaml

• For Machine and deep learning models: after choosing the features and models by updating the config file, Simply run:

python main.py

• For finetuning pretrained models:

python pre_trained_Wav2Vec.py
python pre_trained_HuBERT.py

This will:

• Load the dataset from config.yaml

• Split the dataset into train/validation/test sets

• Extract features

• Train the specified model

• Evaluate model performance

Data Preparation

Your dataset should be in CSV format with the following columns:

Column Name	Description
path	File path to the audio file in .wav formate
label	Class label for the audio sample (for bowel sound patterns: SB, MB, CRS, HS, and Silence period labelled NONE)
patent_id	Identifier for subject grouping

Example CSV File

Below is an example of how your dataset should look:

Bowel Sound patterns Classification (BS_segments.csv)

path	label	patent_id
/101SG_PT_segment_1.wav	SB	101
/102SG_PT_segment_1.wav	MB	102
/103SG_PT_segment_1.wav	NONE	103