Single-channel separation using underdetermined blind autoregressive model and least absolute deviation
Single channel mixing problem based on underdetermined binaural source separation method. The method is executed in one-go without the need of iterative optimization or training phase. It works very fast and does not require any parameter tuning and any a priori data for sources. It has low computational complexity and does not exploit high-order statistics. Independent of initialization and does not need predetermined structure on the sensors.
autoregressive process underdetermined system blind source separation machine learning sparse timefrequency