Abstract
| Original language | English |
|---|---|
| Title of host publication | Latent Variable Analysis and Signal Separation |
| Editors | Petr Tichavský, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadège Thirion-Moreau |
| Publisher | Springer, Cham |
| Pages | 236-246 |
| Number of pages | 11 |
| ISBN (Electronic) | 9783319535470 |
| ISBN (Print) | 9783319535463 |
| DOIs | |
| Publication status | Published - 15 Feb 2017 |
| Externally published | Yes |
| Event | 13th International Conference on Latent Variable Analysis and Signal Separation - Grenoble-Alpes University, Grenoble, France Duration: 21 Feb 2017 → 23 Feb 2017 Conference number: 13 http://www.lva-ica-2017.com/ (Link to Conference Website) |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 10169 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 13th International Conference on Latent Variable Analysis and Signal Separation |
|---|---|
| Abbreviated title | LVA/ICA 2017 |
| Country | France |
| City | Grenoble |
| Period | 21/02/17 → 23/02/17 |
| Internet address |
|
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Discriminative Enhancement for Single Channel Audio Source Separation Using Deep Neural Networks. / Grais, Emad M; Roma, Gerard; Simpson, Andrew JR; Plumbley, Mark D.
Latent Variable Analysis and Signal Separation. ed. / Petr Tichavský; Massoud Babaie-Zadeh; Olivier J.J. Michel; Nadège Thirion-Moreau. Springer, Cham, 2017. p. 236-246 (Lecture Notes in Computer Science; Vol. 10169).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Discriminative Enhancement for Single Channel Audio Source Separation Using Deep Neural Networks
AU - Grais, Emad M
AU - Roma, Gerard
AU - Simpson, Andrew JR
AU - Plumbley, Mark D
PY - 2017/2/15
Y1 - 2017/2/15
N2 - The sources separated by most single channel audio source separation techniques are usually distorted and each separated source contains residual signals from the other sources. To tackle this problem, we propose to enhance the separated sources to decrease the distortion and interference between the separated sources using deep neural networks (DNNs). Two different DNNs are used in this work. The first DNN is used to separate the sources from the mixed signal. The second DNN is used to enhance the separated signals. To consider the interactions between the separated sources, we propose to use a single DNN to enhance all the separated sources together. To reduce the residual signals of one source from the other separated sources (interference), we train the DNN for enhancement discriminatively to maximize the dissimilarity between the predicted sources. The experimental results show that using discriminative enhancement decreases the distortion and interference between the separated sources.
AB - The sources separated by most single channel audio source separation techniques are usually distorted and each separated source contains residual signals from the other sources. To tackle this problem, we propose to enhance the separated sources to decrease the distortion and interference between the separated sources using deep neural networks (DNNs). Two different DNNs are used in this work. The first DNN is used to separate the sources from the mixed signal. The second DNN is used to enhance the separated signals. To consider the interactions between the separated sources, we propose to use a single DNN to enhance all the separated sources together. To reduce the residual signals of one source from the other separated sources (interference), we train the DNN for enhancement discriminatively to maximize the dissimilarity between the predicted sources. The experimental results show that using discriminative enhancement decreases the distortion and interference between the separated sources.
KW - Audio enhancement
KW - Deep neural networks
KW - Discriminative training
KW - Single channel audio source separation
U2 - 10.1007/978-3-319-53547-0_23
DO - 10.1007/978-3-319-53547-0_23
M3 - Conference contribution
SN - 9783319535463
T3 - Lecture Notes in Computer Science
SP - 236
EP - 246
BT - Latent Variable Analysis and Signal Separation
A2 - Tichavský, Petr
A2 - Babaie-Zadeh, Massoud
A2 - Michel, Olivier J.J.
A2 - Thirion-Moreau, Nadège
PB - Springer, Cham
ER -