Adaptive Coding of Non-Negative Factorization Parameters with Application to Informed Source Separation

Abstract

Informed source separation (ISS) uses source separation for extracting audio objects out of their downmix given some pre-computed parameters. In recent years, non-negative tensor factorization (NTF) has proven to be a good choice for compressing audio objects at an encoding stage. At the decoding stage, these parameters are used to separate the downmix with Wiener-filtering. The quantized NTF parameters have to be encoded to a bit stream prior to transmission. In this paper, we propose to use context-based adaptive binary arithmetic coding (CABAC) for this task. CABAC is widely used in the video coding community and exploits local signal statistics. We adapt CABAC to the task of NTF-based ISS and show that our contribution outperforms reference coding methods.