《Multi-Task Multi-Network Joint-Learning of Deep Residual Networks and Cycle-Consistency Generative Adversarial Networks for Robust Speech Recognition》电子版地址

简介: Multi-Task Multi-Network Joint-Learning of Deep Residual Networks and Cycle-Consistency Generative Adversarial Networks for Robust Speech Recognition

《Multi-Task Multi-Network Joint-Learning of Deep Residual Networks and Cycle-Consistency Generative Adversarial Networks for Robust Speech Recognition》Multi-Task Multi-Network Joint-Learning of Deep Residual Networks and Cycle-Consistency Generative Adversarial Networks for Robust Speech Recognition

电子书:

屏幕快照 2022-06-17 上午9.58.35.png

                
            </div>
目录
相关文章
|
1月前
|
机器学习/深度学习 网络协议 PyTorch
【文献学习】DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement
本文介绍了一种新的深度复数卷积递归网络(DCCRN),用于处理语音增强问题,特别是针对低模型复杂度的实时处理。
24 5
|
3月前
|
机器学习/深度学习 TensorFlow 算法框架/工具
MTCNN(Multi-task Cascaded Convolutional Networks)
MTCNN(Multi-task Cascaded Convolutional Networks)
28 0
|
11月前
|
机器学习/深度学习 数据挖掘
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
51 1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
|
机器学习/深度学习 编解码 数据可视化
Speech Emotion Recognition With Local-Global aware Deep Representation Learning论文解读
语音情感识别(SER)通过从语音信号中推断人的情绪和情感状态,在改善人与机器之间的交互方面发挥着至关重要的作用。尽管最近的工作主要集中于从手工制作的特征中挖掘时空信息,但我们探索如何从动态时间尺度中建模语音情绪的时间模式。
120 0
|
机器学习/深度学习 算法 数据挖掘
A Generative Adversarial Network-based Deep Learning Method for Low-quality Defect ImageReconstructi
本文提出了一种基于生成对抗网络 (GAN) 的 DL 方法,用于低质量缺陷图像识别。 GAN用于重建低质量缺陷图像,并建立VGG16网络识别重建图像。
134 0
|
机器学习/深度学习 知识图谱
论文笔记:Multi-dimensional Graph Convolutional Networks
论文笔记:Multi-dimensional Graph Convolutional Networks
174 0
论文笔记:Multi-dimensional Graph Convolutional Networks
《Autoencoder-based Semi-Supervised Curriculum Learning For Out-of-domain Speaker Verification》电子版地址
Autoencoder-based Semi-Supervised Curriculum Learning For Out-of-domain Speaker Verification
73 0
《Autoencoder-based Semi-Supervised Curriculum Learning For Out-of-domain Speaker   Verification》电子版地址
|
机器学习/深度学习
Re22:读论文 HetSANN An Attention-based Graph Neural Network for Heterogeneous Structural Learning
Re22:读论文 HetSANN An Attention-based Graph Neural Network for Heterogeneous Structural Learning
Re22:读论文 HetSANN An Attention-based Graph Neural Network for Heterogeneous Structural Learning
|
机器学习/深度学习 监控 算法
Paper:Xavier参数初始化之《Understanding the difficulty of training deep feedforward neural networks》的翻译与解读
Paper:Xavier参数初始化之《Understanding the difficulty of training deep feedforward neural networks》的翻译与解读
Paper:Xavier参数初始化之《Understanding the difficulty of training deep feedforward neural networks》的翻译与解读
|
存储 机器学习/深度学习 数据挖掘
Multi-Scale Convolutional Neural Networks for Time Series Classification
针对现有时间序列分类方法的特征提取与分类过程分离,且无法提取存在于不同时间尺度序列的不同特征的问题,作者提出MCNN模型。 对于单一时间序列输入,进行降采样和滑动平均等变化,产生多组长度不同的时间序列,并在多组时间序列上进行卷积,提取不同时间尺度序列的特征。
214 0
Multi-Scale Convolutional Neural Networks for Time Series Classification