Probabilistic latent tensor factorization framework for audio modeling

Ali Taylan Cemgil, Umut Simsekli, Yusuf Cem Sübakan

Abstract: This paper introduces probabilistic latent tensor factorization (PLTF) as a general framework for hierarchical modeling of audio. This framework combines practical aspects of graphical modeling of machine learning with tensor factorization models. Once a model is constructed in the PLTF framework, the estimation algorithm is immediately available. We illustrate our approach using several popular models such as NMF or NMF2D and provide extensions with simulation results on real data for key audio processing tasks such as restoration and source separation.