Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation
This is an incremental improvement for audio processing tasks like music signal separation.
The paper tackles multichannel audio source separation by proposing IDLMA, which estimates a demixing matrix and updates source structures using a pretrained DNN, showing validity in separation accuracy and computational cost.
In this paper, we address a multichannel audio source separation task and propose a new efficient method called independent deeply learned matrix analysis (IDLMA). IDLMA estimates the demixing matrix in a blind manner and updates the time-frequency structures of each source using a pretrained deep neural network (DNN). Also, we introduce a complex Student's t-distribution as a generalized source generative model including both complex Gaussian and Cauchy distributions. Experiments are conducted using music signals with a training dataset, and the results show the validity of the proposed method in terms of separation accuracy and computational cost.