Voice Gender Recognition

Overview

This project focuses on training a machine learning model to classify voices as male or female based on their acoustic properties. The dataset used for training consists of 36,168 voice samples collected from a Yandex contest. The best-performing model achieves an accuracy of 98% during cross-validation.

Dataset Information

You can download the raw dataset, which includes .wav files and a corresponding .csv file with labels, from the following link: Raw Dataset.

Alternatively, a pre-processed version of the dataset, containing 3.5k rows (due to hardware limitations), is available here: Pre-processed Dataset.

Acoustic Properties

The following acoustic properties are measured for each voice sample:

• meanfreq: Mean frequency (in kHz)
• sd: Standard deviation of frequency
• median: Median frequency (in kHz)
• Q25: First quantile (in kHz)
• Q75: Third quantile (in kHz)
• IQR: Interquantile range (in kHz)
• skew: Skewness
• kurt: Kurtosis
• sp.ent: Spectral entropy
• sfm: Spectral flatness
• mode: Mode frequency
• centroid: Frequency centroid
• peakf: Peak frequency (frequency with highest energy)
• meanfun: Average fundamental frequency measured across the acoustic signal
• minfun: Minimum fundamental frequency measured across the acoustic signal
• maxfun: Maximum fundamental frequency measured across the acoustic signal
• meandom: Average dominant frequency measured across the acoustic signal
• mindom: Minimum dominant frequency measured across the acoustic signal
• maxdom: Maximum dominant frequency measured across the acoustic signal
• dfrange: Range of dominant frequency measured across the acoustic signal
• modindx: Modulation index, calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range

Technology Stack

The project utilizes the following libraries and tools:

• Pandas: For data manipulation and analysis
• NumPy: For numerical operations
• Seaborn and Matplotlib: For data visualization
• Scikit-learn: For machine learning algorithms and model evaluation
• XGBoost: For gradient boosting algorithms
• Librosa: For audio processing and feature extraction
• Concurrent: For parallel processing
• Logging: For logging errors and warnings

Environment Setup

1. Create a Virtual Environment:

   python -m venv env

2.  source env/bin/activate

3.  pip install -r requirements.txt