Neural Network Alternatives to Convolutive Audio Models for Source Separation
Sound
2017-09-26 v1 Audio and Speech Processing
Abstract
Convolutive Non-Negative Matrix Factorization model factorizes a given audio spectrogram using frequency templates with a temporal dimension. In this paper, we present a convolutional auto-encoder model that acts as a neural network alternative to convolutive NMF. Using the modeling flexibility granted by neural networks, we also explore the idea of using a Recurrent Neural Network in the encoder. Experimental results on speech mixtures from TIMIT dataset indicate that the convolutive architecture provides a significant improvement in separation performance in terms of BSSeval metrics.
Cite
@article{arxiv.1709.07908,
title = {Neural Network Alternatives to Convolutive Audio Models for Source Separation},
author = {Shrikant Venkataramani and Y. Cem Subakan and Paris Smaragdis},
journal= {arXiv preprint arXiv:1709.07908},
year = {2017}
}
Comments
Published in MLSP 2017