A FRAMEWORK FOR SPEECH ENHANCEMENT USING EXTREME LEARNING MACHINES

被引：0

作者：

Odelowo, Babafemi O. ^{[1
]}

Anderson, David V. ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

来源：

2017 FIFTY-FIRST ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS | 2017年

关键词：

Speech enhancement; extreme learning machines (ELM); noise reduction; supervised learning; online sequential ELM (OS-ELM); REGRESSION;

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：

Neural networks have been widely applied in the enhancement of speech degraded by additive noise. In this paper, we study a mask-based speech enhancement framework employing the extreme learning machine (ELM). Experimental results show the proposed framework is extremely effective in suppressing additive noise when trained with small datasets. It is superior to a leading minimum mean-square error (MMSE) algorithm in both matched and mismatched noise conditions with just one hour of training data and performs consistently over a wide range of training conditions. In addition, the mask-based ELM approach is shown to be more effective than the spectral mapping approach.

引用

页码：1956 / 1960

页数：5

共 29 条

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