Comparative Study of Masking and Mapping Based on Hierarchical Extreme Learning Machine for Speech Enhancement

Ryandhimas E. Zezario, Join W.C. Sigalingging, Tassadaq Hussain, Jia Ching Wang, Yu Tsao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this study, we compare the speech enhancement performance based on hierarchical extreme learning machine (HELM) with two distinct strategies: masking and mapping. Experimental results of the perceptual evaluation of speech quality (PESQ) show that for both of limited and sufficient amounts of training data, mapping-based HELM tends to be more effective to improve the performance of speech enhancement.

Original languageEnglish
Title of host publicationProceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728130385
DOIs
StatePublished - Dec 2019
Event2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 - Taipei, Taiwan
Duration: 3 Dec 20196 Dec 2019

Publication series

NameProceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019

Conference

Conference2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
Country/TerritoryTaiwan
CityTaipei
Period3/12/196/12/19

Fingerprint

Dive into the research topics of 'Comparative Study of Masking and Mapping Based on Hierarchical Extreme Learning Machine for Speech Enhancement'. Together they form a unique fingerprint.

Cite this