基于固定先验和重尾语音模型的语音存在概率估计

Balázs Fodor, Timo Gerkmann
{"title":"基于固定先验和重尾语音模型的语音存在概率估计","authors":"Balázs Fodor, Timo Gerkmann","doi":"10.5281/ZENODO.43797","DOIUrl":null,"url":null,"abstract":"Speech enhancement approaches are often enhanced by speech presence probability (SPP) estimation. However, SPP estimators suffer from random fluctuations of the a posteriori signal-to-noise ratio (SNR). While there exist proposals that overcome the random fluctuations by basing the SPP framework on smoothed observations, these approaches do not take into account the super-Gaussian nature of speech signals. Thus, in this paper we define a framework that allows for modeling the likelihoods of speech presence for smoothed observations, while at the same time assuming super-Gaussian speech coefficients. The proposed approach is shown to outperform the reference approaches in terms of the amount of noise leakage and the amount of musical noise.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A speech presence probability estimator based on fixed priors and a heavy-tailed speech model\",\"authors\":\"Balázs Fodor, Timo Gerkmann\",\"doi\":\"10.5281/ZENODO.43797\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Speech enhancement approaches are often enhanced by speech presence probability (SPP) estimation. However, SPP estimators suffer from random fluctuations of the a posteriori signal-to-noise ratio (SNR). While there exist proposals that overcome the random fluctuations by basing the SPP framework on smoothed observations, these approaches do not take into account the super-Gaussian nature of speech signals. Thus, in this paper we define a framework that allows for modeling the likelihoods of speech presence for smoothed observations, while at the same time assuming super-Gaussian speech coefficients. The proposed approach is shown to outperform the reference approaches in terms of the amount of noise leakage and the amount of musical noise.\",\"PeriodicalId\":198408,\"journal\":{\"name\":\"2014 22nd European Signal Processing Conference (EUSIPCO)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 22nd European Signal Processing Conference (EUSIPCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.43797\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 22nd European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.43797","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

语音增强方法通常通过语音存在概率(SPP)估计来增强。然而,SPP估计器受到后验信噪比(SNR)随机波动的影响。虽然有一些建议通过基于平滑观测的SPP框架来克服随机波动,但这些方法没有考虑语音信号的超高斯性质。因此,在本文中,我们定义了一个框架,该框架允许对平滑观察的语音存在的可能性进行建模,同时假设超高斯语音系数。结果表明,该方法在噪声泄漏量和音乐噪声量方面优于参考方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A speech presence probability estimator based on fixed priors and a heavy-tailed speech model
Speech enhancement approaches are often enhanced by speech presence probability (SPP) estimation. However, SPP estimators suffer from random fluctuations of the a posteriori signal-to-noise ratio (SNR). While there exist proposals that overcome the random fluctuations by basing the SPP framework on smoothed observations, these approaches do not take into account the super-Gaussian nature of speech signals. Thus, in this paper we define a framework that allows for modeling the likelihoods of speech presence for smoothed observations, while at the same time assuming super-Gaussian speech coefficients. The proposed approach is shown to outperform the reference approaches in terms of the amount of noise leakage and the amount of musical noise.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信