IEEE Signal Processing Letters

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In this letter, we address an audio signal separation problem and propose a new effective algorithm for solving a bilevel optimization in discriminative nonnegative matrix factorization (NMF). Recently, discriminative training of NMF bases has been developed for better signal separation in supervised NMF (SNMF), which exploits a priori training of given sample signals.

Scope

The IEEE Signal Processing Letters is an archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

The purpose of the publication of articles is to advance the theory, a new novel that will be of both interest and value.

Authors are encouraged to submit manuscripts of LETTERS. Letters are four page articles designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

Submissions/resubmissions must be previously unpublished and may not be under consideration elsewhere.

Every manuscript must:

Christ D. Richmond Editor-in-Chief:
Christ D. Richmond
Duke University, USA
Email EiC
Email SPS Publications Office
Term Ends: 31 December 2025

 

 

 

Updated September 2015
EDICS NAME DESCRIPTION
MLSAS Machine Learning and Statistical Signal processing
SAS-STAT Detection, estimation and classification theory and methods, statistical signal processing
SAS-SYST

IEEE Signal Processing Letters

Scope

The IEEE Signal Processing Letters (SPL) is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.

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