AuthAudio 🎙️

AI-powered audio authentication package that detects whether audio is fake (AI-generated) or real (human) using deep learning.

Features

- 🤖 AI-Powered Detection: Uses a trained neural network to classify audio
- 🎯 High Accuracy: Trained on MFCC features for robust detection
- 📦 Easy to Use: Simple API for Node.js applications
- ⚡ Fast: Efficient TensorFlow.js implementation
- 🔧 Flexible: Works with pre-extracted audio features

Installation

``bash
npm install authaudio
`

Quick Start

`javascript
import AuthAudio from 'authaudio';

// Create classifier instance
const classifier = new AuthAudio();

// Load the model (only needed once)
await classifier.loadModel();

// Predict from pre-extracted MFCC features (40 coefficients)
const features = [/ your 40 MFCC coefficients /];
const result = await classifier.predictFromFeatures(features);

console.log(result);
// Output:
// {
// prediction: 'Human',
// confidence: '95.23%',
// probabilities: {
// human: '95.23%',
// ai: '4.77%'
// },
// raw: {
// human: 0.9523,
// ai: 0.0477
// }
// }
`

Feature Extraction

This package requires pre-extracted MFCC features. Use Python with librosa to extract features:

`python
import librosa
import numpy as np

Load audio file

audio, sr = librosa.load('audio.wav')

Extract 40 MFCC coefficients

mfcc = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=40)

Calculate mean across time (this gives you 40 features)

features = np.mean(mfcc.T, axis=0)

Convert to list for JavaScript

features_list = features.tolist()
print(features_list) # Use these in your Node.js app
`

API Reference

$3

Creates a new AuthAudio classifier instance.

`javascript
const classifier = new AuthAudio();
`

$3

Loads the TensorFlow.js model. Must be called before making predictions.

`javascript
await classifier.loadModel();
`

$3

Predicts whether audio is fake or real from pre-extracted MFCC features.

Parameters:
- features (Array | Float32Array): 40 MFCC coefficients

Returns: Promise