지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 대한전자공학회 ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications ITC-CSCC : 2008
- 발행연도
- 2008.7
- 수록면
- 381 - 384 (4page)
이용수
초록· 키워드
오류제보하기
A merit function based on short length linear predictability of signal in an objective probabilistic algorithm is defined and used for blind source separation (BSS) of linear mixtures of signals. In BSS literatures, it has been conjectured that linear mixture of statistically independent source signals will result in a set of signals which each of them has less predictability than (or equal to) that of any of component source signals. We have used this property to extract source signals by finding an un-mixing matrix that maximizes the proposed merit function of predictability for each recovered signal. This method which is called Probabilistic Short-length Linear Predictability BSS (PSLP-BSS), its performance has been driven with many tests performed with mixtures of different kinds (speech, audio, image, constructed mathematical signals like saw tooth and sinusoidal). In all cases, correlation between each of source signals and each of extracted signals shows near-perfect performance of the method. The proposed BSS doesnt require any assumption regarding the probability density function of source signals. It has been demonstrated that PSLP-BSS can separate signal mixtures in which each mixture is a linear combination of source signals with gaussian, super-gaussian and sub-gaussian probability density functions. However, the method is adapted to temporal structure of recovered signals. Since, the un-mixing matrix that is concluded by proposed merit function can be obtained as the solution to a generalized eigenvalue routine, signals can be extracted simultaneously using the fast eigen value.
목차
Abstract
1. Introduction
2. BSS Problem Formulation
3. Probabilistic Measurement of Predictability
4. PSLP-BSS
5. Results and Discussion
6. Conclusion
References
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Active Noise Cancellation using a Teacher Forced BSS earning Algorithm
센서학회지
2004 .01
-
Identification of fault signal for rotating machinery diagnosis using Blind Source Separation (BSS)
한국소음진동공학회 학술대회논문집
2003 .05
-
Multi-channel Speech Enhancement Using Blind Source Separation and Cross-channel Wiener Filtering
한국음향학회지
2004 .01
-
Application of Blind Deconvolution with Crest Factor for Recovery of
Original Rolling Element Bearing Defect Signals대한기계학회 춘추학술대회
2004 .11
-
백스캐터 통신을 위한 signal to signal 변환 모델
한국정보과학회 학술발표논문집
2020 .07
-
Blind Image Separation with Neural Learning Based on Information Theory and Higher-order Statistics
전기학회논문지
2008 .08
-
자동차 전장음 음질평가 시스템
대한전자공학회 학술대회
2006 .06
-
IEEE802.11 기반 모바일 BSS 및 고정 BSS간 지능적 간섭회피 기법
한국통신학회 학술대회논문집
2011 .11
-
ICA 기법을 이용한 구조물의 진동원 신호 규명
대한조선학회 논문집
2012 .12
-
전자제품의 정보전달을 위한 신호음 적합성 평가
대한인간공학회 학술대회논문집
2007 .05
-
SIGNALLING SYSTEM NO . 7 IN KOREA , " AT & T`s Perspective "
대한전자공학회 워크샵
1988 .01
-
Overlapped Subband-Based Independent Vector Analysis
한국음향학회지
2008 .01
-
Blind Source Separation of Multiple Audio Source Signals using Psychoacoustic Model
ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications
2005 .07
-
이진 마스크 기반 Blind Source Separation을 위한 베이스 시간-주파수 포인트 추정 방법 비교
대한전자공학회 학술대회
2010 .06
-
A Proposal of Blind Signal Separation Algorithm
ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications
2003 .07
-
주파수 및 진폭의 분산 변화 패턴을 고려한 시스템 상태신호 특성 정의
한국CDE학회 학술발표회 논문집
2016 .01
-
Design of a Software-based Multi-Channel GNSS IF Signal Generator
제어로봇시스템학회 국제학술대회 논문집
2008 .10
-
Retrieving the Time History of Displacement from Measured Acceleration Signal
Journal of Mechanical Science and Technology
2003 .02
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2013-569-001139470