A Skip Attention Mechanism for Monaural Singing Voice Separation

This work proposes a simple but effective attention mechanism, namely Skip Attention (SA), for monaural singing voice separation (MSVS). First, the SA, embedded in the convolutional encoder-decoder network (CEDN), realizes an attention-driven and dependency modeling for the repetitive structures of...

Full description

Saved in:
Bibliographic Details
Published in:IEEE signal processing letters 2019-10, Vol.26 (10), p.1481-1485
Main Authors: Yuan, Weitao, Wang, Shengbei, Li, Xiangrui, Unoki, Masashi, Wang, Wenwu
Format: Article
Language:English
Subjects:
Coders
Convolution
Datasets
Decoding
Dependence
Encoders-Decoders
Feature extraction
monaural singing voice separation
Music
Repetitive structures
Separation
Singing
Skip attention
Spectrogram
stacked hourglass network
Time-frequency analysis
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work proposes a simple but effective attention mechanism, namely Skip Attention (SA), for monaural singing voice separation (MSVS). First, the SA, embedded in the convolutional encoder-decoder network (CEDN), realizes an attention-driven and dependency modeling for the repetitive structures of the music source. Second, the SA, replacing the popular skip connection in the CEDN, effectively controls the flow of the low-level (vocal and musical) features to the output and improves the feature sensitivity and accuracy for MSVS. Finally, we implement the proposed SA on the Stacked Hourglass Network (SHN), namely Skip Attention SHN (SA-SHN). Quantitative and qualitative evaluation results have shown that the proposed SA-SHN achieves significant performance improvement on the MIR-1K dataset (compared to the state-of-the-art SHN) and competitive MSVS performance on the DSD100 dataset (compared to the state-of-the-art DenseNet), even without using any data augmentation methods.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2019.2935867