IEEE Signal Processing Letters

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In this letter, we address the problem of simultaneously tracking several moving audio sources, namely the problem of estimating source trajectories from a sequence of observed features. We propose to use the von Mises distribution to model audio-source directions of arrival with circular random variables. This leads to a Bayesian filtering formulation, which is intractable because of the combinatorial explosion of associating observed variables with latent variables, over time. We propose a variational approximation of the filtering distribution.

Aiming at accurate weak sea-surface target detection, this letter devotes to designing a learning-based detector that can work well even in varying detection environments. We first exploit the concept of the fractal theory to extract three representative features in the time and frequency domains and construct a three-dimensional feature space. We then combine the constructed feature space with the decision tree approach to design an environment-adaptive detector.

We investigate the blind identification and separation of underdetermined linear instantaneous mixtures with a single sensor and an arbitrary known number of sources with finite known support and uniform distribution. We propose channel estimators based on the high-order statistics of the received signal and on the rotational symmetries of the source constellations. Explicit expressions for distinct and equal rotation orders are derived. The proposed estimators are used as initializers for the iterative least squares with enumeration algorithm to enhance its convergence properties.

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:

James Fowler Editor-in-Chief:
Z. Jane Wang
The University of British Columbia, Canada
Email EiC
Email SPS Publications Office
Term Ends: 31 December 2022

 

 

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 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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