Automated Audio Analysis Pipeline

Overview

This project provides a scalable pipeline for audio data analysis designed for large-scale survey datasets.
It automates the preprocessing, feature extraction, and parallelized computation of thousands of audio files, improving efficiency, data quality, and reproducibility in research workflows.

The project was initially developed in R using packages such as soundgen, tuneR, and parallel, and is tailored for applications in survey monitoring, evaluation, and development research.

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Objectives

• Automate the processing of raw audio files collected during surveys.
• Extract acoustic features (duration, pitch, intensity, formants, etc.).
• Use parallelization to handle thousands of files efficiently.
• Provide reproducible scripts for large-scale monitoring and evaluation projects.
• Enable researchers and practitioners to transform raw sound into actionable insights.

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Methodology

1. Data Input – Load audio files from survey datasets (.wav).
2. Preprocessing – Standardize sampling rates and clean files.
3. Feature Extraction – Acoustic analysis using soundgen and tuneR.
4. Parallelized Processing – Speed up computation by distributing tasks across multiple cores.
5. Output – Structured datasets ready for statistical analysis or machine learning models.

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Installation & Requirements

1. Clone the repository

git clone https://github.com/<your-username>/<repo-name>.git
cd <repo-name>