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ICASSP 2016 presentation, Session: IVMP-P8 - Interpolation and Super-Resolution, Tursday, March 24, 13:30-15:30

ICASSP 2016 presentation, Session: SAM-P1 - MIMO Radar, Tuesday, March 22, 16:00-18:00

ICASSP 2016 presentation, Session: SPTM-P14 - Compressed Sampling and Sparsity, Friday, March 25, 8:30-10:30

Abstract—In most applications of sinusoidal models for speech
signal, an amplitude spectral envelope is necessary. This envelope
is not only assumed to fit the vocal tract filter response as
accurately as possible, but it should also exhibit slow varying
shapes across time. Indeed, time irregularities can generate
artifacts in signal manipulations or increase improperly the
features variance used in statistical models. In this letter, a
simple technique is suggested to improve this time regularity.

Filter Bank Multi-Carrier (FBMC) modulation is considered as a possible candidate for 5G. Compared to OFDM, channel estimation becomes more challenging in FBMC due to the imaginary interference, which has to be canceled at the pilot positions either by auxiliary pilot symbols or coding.

We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an input signal, each regressor estimates the onset and offset positions of the target event. The estimation confidence scores output by a regressor are then used to quantify how the target event aligns with the temporal structure of the corresponding category.

In this paper we focus on the relative position and orientation estimation between rigid bodies in an anchorless scenario. Several sensor units are installed on the rigid platforms, and the sensor placement on the rigid bodies is known beforehand (i.e., relative locations of the sensors on the rigid body are known). However, the absolute position of the rigid bodies is not known. We show that the relative localization of rigid bodies amounts to the estimation of a rotation matrix and the relative distance between the centroids of the rigid bodies.

We propose a new algorithm to learn a dictionary along with sparse representations from signal measurements without phase. Specifically, we consider the task of reconstructing a two-dimensional image from squared-magnitude measurements of a complex-valued linear transformation of the original image. Several recent phase retrieval algorithms exploit underlying sparsity of the unknown signal in order to improve recovery performance.

In video surveillance as well as automotive applications, so-called fisheye cameras are often employed to capture a very wide angle of view. To be able to develop and evaluate algorithms specifically adapted to fisheye images and videos, a corresponding test data set is therefore introduced in this paper. The sequences are freely available via www.lms.lnt.de/fisheyedataset/.