Music industry is increasingly threatened by AI-generated vocal deepfakes that can mimic any artist's voice with alarming accuracy. An AI-generated track featuring fake Drake and The Weeknd vocals hit 600,000 streams before removal, one incident among thousands. Tools like RVC and GPT-SoVITS now produce convincing vocal clones in hours, leaving musicians vulnerable to unauthorized AI performances and streaming platforms without reliable detection methods. Our poster demonstrates how Python's scientific computing ecosystem can help distinguish authentic performances from AI-generated imitations.
By using Python libraries like Librosa and PyTorch, we analyze audio at a level beyond human perception, detecting subtle synthesis artifacts that reveal synthetic origins.
Imagine streaming platforms automatically flagging unauthorized AI covers, or artists having tools to verify their voice hasn't been cloned. Our approach uses spectral analysis to capture vocal characteristics - from vibrato patterns to formant transitions to micro-variations in breath sounds - and siamese networks to learn each artist's unique vocal fingerprint, identifying patterns that distinguish real performances from convincing fakes.
The poster will bridge audio signal processing with practical content verification, providing an interactive demonstration of how machine learning can protect artist identity in an era of generative AI.