University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation

Grais, Emad M., Zhao, Fei and Plumbley, Mark D. (2020) Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation In: 28th European Signal Processing Conference (EUSIPCO 2020), 18-21 Jan 2021, Amsterdam, The Netherlands.

[img] Text
GraisZhaoPlumbley20-eusipco_accepted.pdf - Accepted version Manuscript
Restricted to Repository staff only until 18 January 2021.

Download (4MB)

Abstract

Deep neural networks with convolutional layers usually process the entire spectrogram of an audio signal with the same time-frequency resolutions, number of filters, and dimensionality reduction scale. According to the constant-Q transform, good features can be extracted from audio signals if the low frequency bands are processed with high frequency resolution filters and the high frequency bands with high time resolution filters. In the spectrogram of a mixture of singing voices and music signals, there is usually more information about the voice in the low frequency bands than the high frequency bands. These raise the need for processing each part of the spectrogram differently. In this paper, we propose a multi-band multi-resolution fully convolutional neural network (MBR-FCN) for singing voice separation. The MBR-FCN processes the frequency bands that have more information about the target signals with more filters and smaller dimensionality reduction scale than the bands with less information. Furthermore, the MBR-FCN processes the low frequency bands with high frequency resolution filters and the high frequency bands with high time resolution filters. Our experimental results show that the proposed MBRFCN with very few parameters achieves better singing voice separation performance than other deep neural networks.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Grais, Emad M.
Zhao, Fei
Plumbley, Mark D.m.plumbley@surrey.ac.uk
Date : 29 May 2020
Additional Information : Embargo OK Metadata Pending
Depositing User : James Marshall
Date Deposited : 04 Aug 2020 08:46
Last Modified : 04 Aug 2020 08:46
URI: http://epubs.surrey.ac.uk/id/eprint/858351

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800