Binarization of music score with complex background by deep convolutional neural networks
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1573-7721
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Binarization of music score with complex background by deep convolutional neural networks ; day:3 ; month:1 ; year:2021 ; pages:1-17
Multimedia tools and applications ; (3.1.2021), 1-17
- Creator
-
Tran, Minh-Trieu
Vo, Quang-Nhat
Lee, Guee-Sang
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s11042-020-10272-2
- URN
-
urn:nbn:de:101:1-2021020417555022869743
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:28 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Tran, Minh-Trieu
- Vo, Quang-Nhat
- Lee, Guee-Sang
- SpringerLink (Online service)
Other Objects (12)
Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks
Sign language recognition based on dual-path background erasure convolutional neural network
Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
Emotion and themes recognition in music utilising convolutional and recurrent neural networks
The effects of background music on neural responses during reading comprehension
Neural encoding with unsupervised spiking convolutional neural network
CONEqNet: convolutional music equalizer network
Music detection from broadcast contents using convolutional neural networks with a Mel-scale kernel
Optimization of the convolutional neural network classification model under the background of innovative art teaching models
Deepfake detection using convolutional vision transformers and convolutional neural networks
Digital Music Industry – Background Synthesis
Phylogenetic convolutional neural networks in metagenomics
Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks
Sign language recognition based on dual-path background erasure convolutional neural network
Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
Emotion and themes recognition in music utilising convolutional and recurrent neural networks
The effects of background music on neural responses during reading comprehension
Neural encoding with unsupervised spiking convolutional neural network
CONEqNet: convolutional music equalizer network
Music detection from broadcast contents using convolutional neural networks with a Mel-scale kernel
Optimization of the convolutional neural network classification model under the background of innovative art teaching models
Deepfake detection using convolutional vision transformers and convolutional neural networks
Digital Music Industry – Background Synthesis
Phylogenetic convolutional neural networks in metagenomics
Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks
Sign language recognition based on dual-path background erasure convolutional neural network
Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
Emotion and themes recognition in music utilising convolutional and recurrent neural networks
The effects of background music on neural responses during reading comprehension
Neural encoding with unsupervised spiking convolutional neural network
CONEqNet: convolutional music equalizer network
Music detection from broadcast contents using convolutional neural networks with a Mel-scale kernel
Optimization of the convolutional neural network classification model under the background of innovative art teaching models
Deepfake detection using convolutional vision transformers and convolutional neural networks
Digital Music Industry – Background Synthesis