A convolutional recurrent neural network with attention framework for speech separation in monaural recordings
Abstract Most speech separation studies in monaural channel use only a single type of network, and the separation effect is typically not satisfactory, posing difficulties for high quality speech separation. In this study, we propose a convolutional recurrent neural network with an attention (CRNN-A...
Enregistré dans:
Auteurs principaux: | Chao Sun, Min Zhang, Ruijuan Wu, Junhong Lu, Guo Xian, Qin Yu, Xiaofeng Gong, Ruisen Luo |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/d0e7c3a33d6b4689a893b61ff127b46f |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Bangla hate speech detection on social media using attention-based recurrent neural network
par: Das Amit Kumar, et autres
Publié: (2021) -
Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling
par: Jou-Kou Wang, et autres
Publié: (2020) -
Robust feature space separation for deep convolutional neural network training
par: Ali Sekmen, et autres
Publié: (2021) -
An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
par: Muhammad Zakir Ullah, et autres
Publié: (2021) -
A combined convolutional and recurrent neural network for enhanced glaucoma detection
par: Soheila Gheisari, et autres
Publié: (2021)