Features Extraction Module

This module is an essential part of a system designed to identify deepfake audio recordings. It contains all the necessary tools for extracting a wide range of audio features that help differentiate real audio from synthetic ones.

Purpose

The main purpose of this module is to provide a reliable framework for pulling various audio features from raw sound data. These features include spectral, prosodic, and voice quality aspects, each offering valuable insights into the characteristics of audio signals. By examining these features, the system can identify patterns and irregularities that are commonly found in deepfake audio, improving the effectiveness of detection methods.

Modular Design

The module's design is highly modular, making it easy to integrate and scale. Researchers and developers can conveniently expand existing feature categories or add new ones as new developments in audio analysis become available. This flexibility ensures that the system can adapt and evolve without needing major changes, supporting ongoing improvements in deepfake detection technology.

Module Overview

Directory Structure

__init__.py: Initializes the features.extraction package, allowing classes and functions to be imported.
features_list.py: Manages lists of feature names used throughout the various extraction modules.
high_level_features_extractor.py: Extracts complex statistical features from detailed low-level features.
low_level_features_extractor.py: Pulls detailed low-level features from raw audio data.
prosodic_features.py: Focuses on features related to the rhythm and intonation of speech.
spectral_features.py: Extracts features that describe the audio spectrum.
voice_quality_features.py: Collects features that show the quality and traits of the voice.
stat_measures.py: Provides statistical analysis tools to compute measures such as mean, variance, and skewness for extracted detailed low-level features.

Workflow

1. Input

The workflow starts with raw audio data which is preprocessed to get it ready for feature extraction.

2. Detailed Low-Level Feature Extraction

This phase involves pulling out various audio features:

Spectral Features: Such as MFCCs, FFT, and spectral centroids.
Temporal Features: Like zero-crossing rate and peak amplitude.
Prosodic Features: Including measures like speaking rate and pitch.
Voice Quality Features: Evaluating quality through metrics like jitter and shimmer.

3. Transformation and Aggregation

After extracting features, the data is summarized statistically and segmented (sometimes using rolling windows). This helps focus on the most informative parts of the features and reduces the amount of data.

4. High-Level Feature Extraction

The summarized data is then used to compute higher-order statistical features such as mean, standard deviation, skewness, and kurtosis, providing a summary suitable for machine learning models.

5. Output

The end result is a structured array of high-level features for each audio sample, ready for further analysis or direct use in machine learning algorithms.

Usage

from features.extraction.low_level_features_extractor import LowLevelFeatureExtractor
from features.extraction.high_level_features_extractor import HighLevelFeatureExtractor

# Initialize extractors
low_level_extractor = LowLevelFeatureExtractor()
high_level_extractor = HighLevelFeatureExtractor()

# Process audio data
audio_data = {'audio_arr': your_audio_array, 'srate': your_sampling_rate}
low_level_features = low_level_extractor.extract_features(audio_data)
high_level_features = high_level_extractor.compute high_level_features(low_level_features)

print(high_level_features)

Future Integration

The module is designed for easy integration with data preprocessing pipelines and machine learning frameworks. It allows for simple updates, such as adding new feature categories or improving existing ones, ensuring the system stays current with the latest in audio analysis. This flexibility is particularly important for building a reliable audio deepfake detection system.