SDSC/open-pulse-hackathon-data-analysis huggingface dataset:
Repository: marcgj/LAUZHACK23-OpenSystems
Description: Our solution for the Open Systems challenge in LauzHack 2023 in EPFL
Primary language: JavaScript

# LAUZHACK23-OpenSystems 🚀
Our solution for Open Systems challenge in LauzHack 2023 in EPFL

##Overview
Our goal is permitting non professional or non techical persons to retrieve information from different LOGs. Usually, enterprises needs techical teams to communicate with the ones who are not so technical,
using our solution, this part wouldn't be necessary and a lot of time would be saved.

## Key Features ⚒️
**AI-Powered 🤖:**
   - Accurate responses based on the data provided.
   - Data-driven insights for informed decision-making.

**Natural Language Processing 🔍:**
   - Natural Language Queries.
   - Effortlessly data insights.

## Developers </> 
- Alexandru Cristian Stoia - [@aleex0309](https://github.com/aleex0309)
- Marc Gaspà Joval - [@marcgj](https://github.com/marcgj)
- Rafel Salgueiro Santacreu [@rafelsalgueiro](https://github.com/rafelsalgueiro)
Repository: martina-ignacia-hernandez/LauzHack
Primary language: Jupyter Notebook

# Fraud AI Detector

This project is an AI-Generated Fake Audio Detector designed to identify fraudulent or synthetic audio content, particularly in the context of phone calls. Its primary goal is to enhance communication security by detecting AI-generated audios that could be used in scams or fraudulent schemes.

The system processes 5-second audio segments from phone calls, extracts critical audio features, and uses machine learning or deep learning models to determine if the audio is real or generated by AI. This is especially useful for organizations or individuals looking to protect themselves against the growing threats of voice spoofing and synthetic speech technologies.

Key capabilities of the project include:

Detection of AI-generated audio: Uses advanced analysis techniques to differentiate between real and synthetic speech.
Efficient processing: Operates on small audio segments, enabling rapid evaluation of phone call content.
Feature extraction: Employs state-of-the-art methods to capture distinctive characteristics of speech, such as MFCCs (Mel Frequency Cepstral Coefficients), Chroma features, and Mel spectrograms.
Model-based classification: Implements trained models for real-time classification, which can be adapted for specific use cases with new data.

Other applications could be:

Verification systems: Ensuring the authenticity of voice-based transactions or communications.
Voice security: Detecting and mitigating vulnerabilities in systems reliant on voice interactions.

The repository includes tools for preprocessing audio, extracting features, training classification models, and making predictions on unseen data. It is built with scalability and ease of use in mind, allowing organizations to integrate the solution into their communication workflows or security systems.