Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation
Abstract
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.
Keywords
Cite
@article{arxiv.1806.10307,
title = {Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation},
author = {Shinichi Mogami and Hayato Sumino and Daichi Kitamura and Norihiro Takamune and Shinnosuke Takamichi and Hiroshi Saruwatari and Nobutaka Ono},
journal= {arXiv preprint arXiv:1806.10307},
year = {2018}
}
Comments
5 pages, 4 figures, To appear in the Proceedings of the 26th European Signal Processing Conference (EUSIPCO 2018)