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Orthogonal Frequency Division Multiplexing (OFDM) is a modulation technique which is now widely used in various high speed mobile and wireless communication systems. In this paper we show the design and implementation of an OFDM transceiver for the IEEE 802.20 standard, featuring a variable length FFT, and targeting a low cost Xilinx® Spartan™ 3A DSP Field Programmable Gate Array (FPGA). The design...
A robust music genre classification framework is proposed that combines the rich, psycho-physiologically grounded properties of slow temporal modulations of music recordings and the power of sparse representation-based classifiers. Linear subspace dimensionality reduction techniques are shown to play a crucial role within the framework under study. The proposed method yields a music genre classification...
Enhancements to the classical Waveform Similarity Overlap-Add (WSOLA) algorithm are proposed. As a time-domain approach, it works best for small speed changes and quasi-periodic, monophonic signals. Some of our enhancements are especially effective for small, others for large speed changes. As a consequence, significant improvements for all scaling factors are achieved extending the usability of the...
This paper describes a system to transcribe multitimbral polyphonic music based on a joint multiple-F0 estimation. In a frame level, all possible fundamental frequency (F0) candidates are selected. Using a competitive strategy, a spectral envelope is estimated for each combination composed of F0 candidates under assumption that a polyphonic sound can be modeled by a sum of weighted gaussian mixture...
We propose a new approach to solo/accompaniment separation from stereophonic music recordings which extends a monophonic algorithm we recently proposed. The solo part is modelled using a source/filter model to which we added two contributions: an explicit smoothing strategy for the filter frequency responses and an unvoicing model to catch the stochastic parts of the solo voice. The accompaniment...
We compare in this paper diverse hierarchical and multi-class approaches for the speech/music segmentation task, based on Support Vector Machines, combined with a median filter post-processing. We show the effciency of kernel tuning through the novel Kernel Target Alignment criterion. Quantitative results provide an F-measure of 96.9%, that represents an error reduction of about 50% compared to the...
This paper addresses the problem of generating a super-resolution (SR) image from a single multi-valued low-resolution (LR) input image. The main application in our case lies in the exploitation of the cinema or TV archives for projections in higher resolutions (HD, 2K, 4K). We approach this problem from the perspective of image geometry-oriented interpolation. First, the geometry of the LR image...
Magnetic Resonance Imaging (MRI) image reconstruction based on a frequency domain Super-Resolution (SR) algorithm, is presented in the paper. It is shown that the approach improves MRI spatial resolution in cases when Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) sequences are used. The PROPELLER MRI method collects data in rectangular ‘blades’ rotated around...
The extension of lattice based operators to multivariate images is still a challenging theme in mathematical morphology. In this paper, we propose to explicitly construct complete lattices and replace each element of a multivariate image by its rank, creating a rank image suitable for classical morphological processing. Manifold learning is considered as the basis for the construction of a complete...
This paper provides a SURE optimization for the parameters of a sub-class of smooth sigmoid based shrinkage functions. The optimization is performed on an unbiased estimation risk obtained by using sigmoid shrinkage functions. The SURE sigmoid shrinkage performance measurements are compared to those of the SURELET (SURE linear expansion of thresholds) parameterization. It is shown that the SURE sigmoid...
Diffusion-type algorithms have been integrated in recent years successfully into the toolbox of image processing. We introduce a new more flexible and powerful family of parabolic-hyperbolic partial differential equations (PDEs) that somewhat resembles the structure of the parabolic diffusion equation, but incorporates the second order derivative in time. It is instructive to consider intuitively...
Quantizing real-valued templates into binary strings is a fundamental step in biometric compression and template protection. In this paper, we introduce the area under the FRR curve optimize bit allocation (AUF-OBA) principle. Given the bit error probability, AUF-OBA assigns the numbers of quantization bits to every feature, in such way that the analytical area under the false rejection rate (FRR)...
This paper presents a new approach for 3D face modeling and recognition. Motivated by finding a representation that embodies a high power of discrimination between face classes, a new type of 3D shape descriptors is suggested. We have developed a fully automatic system which uses an alignment algorithm to register 3D facial scans. In addition, scalability in both time and space is achieved by converting...
In this paper we investigate the performance of different feature extraction methods for facial expression recognition based on the higher-order local autocorrelation (HLAC) coefficients and local binary pattern (LBP) operator. Autocorrelation coefficients are computationally inexpensive, inherently shift-invariant and quite robust against changes in facial expression. The focus is on the difficult...
Permanence of biometric features for face verification remains a largely open research problem. Actual and up-to-date at the time of their creation, extracted features and models relevant to a person's face may eventually become outdated, leading to a failure in the face verification task. If physical characteristics of the individual change over time, their classification model has to be updated...
Automatically recognizing humans using their biometric traits such as face and fingerprint will have very important implications in our daily lives. This problem is challenging because biometric traits can be affected by the acquisition process which is sensitive to the environmental conditions (e.g., lighting) and the user interaction. It has been shown that post-processing the classifier output,...
Belief propagation (BP), also called “sum-product algorithm”, is one of the best-known graphical model for inference in statistical physics, artificial intelligence, computer vision, etc. Furthermore, a recent research in distributed sensor network localization showed us that BP is an efficient way to obtain sensor location as well as appropriate uncertainty. However, BP convergence is not guaranteed...
Simultaneous localization and tracking (SLAT) in sensor networks aims to determine the positions of sensor nodes and a moving target in a network, given incomplete and inaccurate range measurements. One of the established methods for achieving this goal is to maximize a likelihood function (ML), which requires initialization with an approximate solution to avoid convergence towards local extrema....
In this paper, we study a new GNSS/INS tight integration algorithm. The underlying idea is to obtain user's position as the solution to a constrained version of the GNSS optimization problem, where classical trilateralization is constrained by INS measurements. This leads to a linearly constrained least-squares problem that can be efficiently solved in worst-case polynomial time. The performance of...
The paper studies the theoretical error lower bound of the mobile tracking problem in mixed Line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. A new method is presented to compute the posterior Cramer-Rao lower bound (CRLB): the mobile state is first estimated by decentralized EKF method, then sigma point set and unscented transformation are applied to calculate Fisher information matrix...
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