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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chan, T.-S.T. Yi-Hsuan Yang |
| Copyright Year | 1994 |
| Abstract | Recently, the principal component pursuit has received increasing attention in signal processing research ranging from source separation to video surveillance. So far, all existing formulations are real-valued and lack the concept of phase, which is inherent in inputs such as complex spectrograms or color images. Thus, in this letter, we extend principal component pursuit to the complex and quaternionic cases to account for the missing phase information. Specifically, we present both complex and quaternionic proximity operators for the ℓ1- and trace-norm regularizers. These operators can be used in conjunction with proximal minimization methods such as the inexact augmented Lagrange multiplier algorithm. The new algorithms are then applied to the singing voice separation problem, which aims to separate the singing voice from the instrumental accompaniment. Results on the iKala and MSD100 datasets confirmed the usefulness of phase information in principal component pursuit. |
| Sponsorship | IEEE Signal Processing Society |
| Starting Page | 287 |
| Ending Page | 291 |
| Page Count | 5 |
| File Size | 234589 |
| File Format | |
| ISSN | 10709908 |
| Volume Number | 23 |
| Issue Number | 2 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2016-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Quaternions Signal processing algorithms Spectrogram Source separation Instruments source separation principal component pursuit algorithms Principal component quaternions |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Signal Processing Electrical and Electronic Engineering |
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