Independent Vector Analysis based Convolutive Speech Separation by Estimating Entropy using Recursive Copula Splitting

被引：0

作者：

Masood, Asim ^{[1
]}

Tong, Renjie ^{[2
]}

Shakeel, Muhammad ^{[3
]}

Ye, Zhongfu ^{[1
]}

机构：

[1] Univ Sci & Technol China, Natl Engn Lab Speech & Language Informat Proc, Hefei 230026, Anhui, Peoples R China

[2] Hikvis Res Inst, Hikvis Headquarters, 555 Qianmo Rd, Hangzhou 310051, Zhejiang, Peoples R China

[3] Univ Lahore, Elect Engn Dept, Islamabad Campus, Islamabad 44000, Pakistan

来源：

PROCEEDINGS OF 2020 IEEE 15TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP 2020) | 2020年

基金：

中国国家自然科学基金;

关键词：

Independent Vector Analysis; Convolutive mixture; Entropy estimation; Recursive copula splitting;

D O I：

10.1109/ICSP48669.2020.9321020

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Speeches in real world environment are normally mixed together convolutedly. The speech mixtures are not instantaneous at all. Rather speeches are mixed component wise. Independent Vector Analysis (IVA) is an approach for separating convolutive mixture in frequency domain. Entropy estimation is an important part of IVA. In this paper, IVA is implemented by estimating entropy using recursive copula splitting. It measures entropy by decomposing probability density function (PDF) into a product of marginal (1D) densities and a copula. This entropy estimator improves IVA performance in different types of real world speech mixtures. We have proved that by estimating entropy using recursive copula splitting makes IVA algorithm simple and more efficient.

引用

页码：646 / 651

页数：6

共 27 条

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