Abstract
Source separation remains a challenging problem, made even more challenging when the mixtures are distorted. We present a novel framework for the source separation from mixtures affected by clipping distortion. Our method combines ℓ1 minimization with knowledge about the geometry of clipped mixtures when the sources are disjoint in time. Our algorithm recovers the sources by solving a convex optimization problem, which is constrained by the clipping geometry. Comparative evaluation experiments show a significant increase in objective recovery performance of our proposed joint method compared to sequential approaches for speech and synthetic signals.
Original language | English |
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Title of host publication | 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (2017) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 220-225 |
Number of pages | 6 |
ISBN (Electronic) | 9781538646625 |
ISBN (Print) | 9781538646632 |
DOIs | |
Publication status | Published - 18 Jun 2018 |
Event | 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 - Bilbao, Spain Duration: 18 Dec 2017 → 20 Dec 2017 |
Conference
Conference | 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 |
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Country/Territory | Spain |
City | Bilbao |
Period | 18/12/17 → 20/12/17 |
Keywords
- Optimization
- Mathematical model
- Estimation
- Quantization (signal)
- Minimization
- Information technology
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Energy Engineering and Power Technology
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Signal Processing