INFORMED SOURCE SEPARATION BY COMPRESSIVE GRAPH SIGNAL SAMPLING



INFORMED SOURCE SEPARATION BY COMPRESSIVE GRAPH SIGNAL SAMPLING

INFORMED SOURCE SEPARATION BY COMPRESSIVE GRAPH SIGNAL SAMPLING
Research Paper / ICASSP 2017 / Mar 2017 / Audio Processing, Machine/Deep Learning/AI

We propose a novel informed source separation method for audio object coding based on a recent sampling theory for smooth signals on graphs. Assuming that only one source is active at each time-frequency point, we compute an ideal map indicating which source is active at each time-frequency point at the encoder. This map is then sampled with a compressive graph signal sampling strategy that guarantees accurate and stable recovery at the decoder. The graph is built using feature vectors, computed using non-negative matrix factorization, that allows us to connect similar source activations in the time-frequency plane. We show that the proposed approach performs better than state-of-the-art methods at low bitrate.