Classification of Deep Fake Audio Using MFCC Technique

Voice forgery, often made possible by advanced generative models, is becoming a bigger problem in today's digital world. There's a urgent demand for a better way to spot AI-generated voices. Our research presents a new method for detecting fake audio by combining advanced signal processing and deep learning techniques. We analyzed a dataset containing both real and fake audio samples by extracting Mel-Frequency Cepstral Coefficients (MFCC) to understand the sound patterns. Using sophisticated neural networks like Convolutional Neural Networks (CNNs) and Fully Connected Layers, we examined voice samples on a large scale. Through systematic testing, we identified the most effective models for distinguishing real from fake audio. Our system includes a user-friendly interface for easy audio upload and analysis. This research advances audio forensics and helps combat the spread of deepfake content online.