Voice And Audio Processing Final Project

This repository contains the final project for the Speech Segmentation course. The goal is to detect and segment speech (foreground) versus background noise from audio files using classification and post-processing techniques.

Programming Languages & Libraries:

• Python 3.8+
• Scikit-learn
• Librosa
• Numpy
• Matplotlib
• Pandas
• PyTorch
• SciPy

Features

• MFCC feature extraction
• MLP-based classifier
• Ridge regression (Least Squares) classifier
• Frame-level evaluation and classification
• CSV export of segment classifications
• JSON ground truth comparison
• Waveform & prediction visualization

File Structure:

speech_segmentation_project/
├── data/
|   ├── features_dataset.npz
│   ├── train/
│   │   ├── speech/
│   │   └── noise/
│   └── test/
│       └── file1.wav
├── src/
│   ├── extract_features.py
│   └── predict_and_generate_csv.py
├── outputs/
│   └── predictions.csv
├── main.py
├── LICENCE.txt
├── requirements.txt
└── README.md

How to Run:

Install the required libraries:

pip install -r requirements.txt

Then run:

python main.py

Export：

outputs/predictions.csv

Data Sources:

• MUSAN Corpus
• CHiME Dataset

Last Updated

This README was last updated on [5/16/2025].