Speech Enhancement Network with Unsupervised Attention using Invariant Information Clustering

被引：0

作者：

Sugiura, Yosuke ^{[1
]}

Nagamori, Shunta ^{[1
]}

Shimamura, Tetsuya ^{[1
]}

机构：

[1] Saitama Univ, Fac Engn, Saitama, Japan

来源：

2021 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC) | 2021年

关键词：

GENERATIVE ADVERSARIAL NETWORKS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose a new framework for speech enhancement using supervised attention trained by Invariant Information Clustering (IIC). For suppressing an overfitting in the speech enhancement network, the multitask learning with the speaker-invariant information is adopted at the latent representation layer. Several simulations reveal the effectiveness of this method through the speech enhancement experiments.

引用

页码：406 / 409

页数：4

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