Emilian Postolache

Emilian Postolache

PhD Student

Sapienza, University of Rome

I am a Ph.D. student interested in generative models and audio processing. My work focused on enabling source separation using latent autoregressive models in VQ-VAE domains, proposing a Bayesian sampling technique based on fully discrete likelihood functions. I pursued an internship at Dolby Laboratories, where I improved universal sound separation using adversarial techniques. I developed a diffusion-based model that simultaneously performs music generation, accompaniment generation, and source separation. Now I am an academic visitor at Queen Mary University of London in the Artificial Intelligence for Music (AIM) group at the Center for Digital Music (C4DM).

  • Generative models
  • Signal processing
  • Source separation