This project reimplement in TensorFlow an audio style transfer algorithm that uses convolutional networks with random weights to extract and recombine content and style features from audio files. Users provide content and style audio files, and the system generates a new audio file that preserves the content structure while adopting the style characteristics of the reference audio. It runs as a Jupyter notebook and exposes an ALPHA parameter to control the balance between content preservation and style stylization.