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Section: Events

Lecture Date: July 25, 2019 Chapter: Columbus Chapter Chair: Philip Schniter Topic: Cyber Attacks on Internet of Things Sensor Systems for Inference

Section: Publications & Resources

The SPS Awards Board is now accepting nominations for all Society level awards, from paper awards to the major society awards. Nominations are due by 1 September 2019.  Nominators should take into consideration the need for representation of diversity in the award nomination slate, when submitting their nominations.  

Section: Publications & Resources

The SPS Awards Board is now accepting nominations for all Society level awards, from paper awards to the major society awards. Nominations are due by 1 September 2019.  Nominators should take into consideration the need for representation of diversity in the award nomination slate, when submitting their nominations.  

Section: Publications & Resources

The SPS Awards Board is now accepting nominations for all Society level awards, from paper awards to the major society awards. Nominations are due by 1 September 2019.  Nominators should take into consideration the need for representation of diversity in the award nomination slate, when submitting their nominations.  

Section: Publications & Resources

The SPS Awards Board is now accepting nominations for all Society level awards, from paper awards to the major society awards. Nominations are due by 1 September 2019.  Nominators should take into consideration the need for representation of diversity in the award nomination slate, when submitting their nominations.  

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

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.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

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.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

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.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

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.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

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.