Yener Ulker

Multiple model target tracking with variable rate particle filters

Article history: Available online 10 January 2012

Annealed SMC Samplers for Nonparametric Bayesian Mixture Models

We develop a novel online algorithm for posterior inference in Dirichlet Process Mixtures (DPM). Our method is based on the Sequential Monte Carlo (SMC) samplers framework that generalizes sequential importance sampling approaches. Unlike the existing methods, the framework enables us to retrospectively update long trajectories in the light of recent observations and this leads to sophisticated clustering update schemes and annealing strategies that seem to prevent the algorithm to get stuck around a local mode. The performance has been evaluated on a Bayesian Gaussian density estimation problem with an unknown number of mixture components. Our simulations suggest that the proposed annealing strategy outperforms conventional samplers. It also provides significantly smaller Monte Carlo standard error with respect to particle filtering given comparable computational resources.

A multiple model structure for tracking by variable rate particle filters

In contrast to the fixed rate modeling of the conventional methods, recently introduced variable rate particle filters (VRPF) achieves to track maneuvering objects with a small number of states by imposing a probability distribution on state arrival times. Although this enables VRPF an appealing method, representing the target motion dynamics with a single model hinders the capability of estimating maneuver parameters precisely. To overcome this weakness we have incorporated multiple model approach with the variable rate model structure. The introduced model referred as Multiple Model Variable Rate Particle Filter (MM-VRPF) utilizes a parsimonious representation for smooth regions of trajectory while it adaptively locates frequent state points at high maneuver regions, resulting in a much more accurate tracking. Simulation results obtained in a bearings-only target tracking problem show that the proposed model outperforms the conventional VRPF, the fixed rate multiple model particle filters (MMPF) and interacting multiple model using extended Kalman filters (IMM-EKF).

A statistical framework for audio watermark detection and decoding

This paper introduces an integrated GMM-based blind audio watermark (WM) detection and decoding scheme that eliminates the decision threshold specification problem which constitutes drawback of the conventional decoders. The proposed method models the statistics of watermarked and original audio signals by Gaussian mixture models (GMM) with K components. Learning of the WM data is achieved in wavelet domain and a Maximum Likelihood (ML) classifier is designed for the WM decoding. Dimension of the learning space is optimized by PCA transformation. Robustness to compression, additive noise and the Stirmark benchmark attacks has been evaluated. It is shown that both WM decoding and detection performance of the introduced integrated scheme outperforms conventional correlation-based decoders. Test results demonstrate that learning in the wavelet domain improves robustness to attacks while reducing complexity. Although performance of the proposed GMM-modeling is slightly better than the SVM-based decoder introduced in [1], significant decrease in computational complexity makes the new method appealing.

Annealed SMC Samplers for Dirichlet Process Mixture Models

In this work we propose a novel algorithm that approximates sequentially the Dirichlet Process Mixtures (DPM) model posterior. The proposed method takes advantage of the Sequential Monte Carlo (SMC) samplers framework to design an effective annealing procedure that prevents the algorithm to get trapped in a local mode. We evaluate the performance in a Bayesian density estimation problem with unknown number of components. The simulation results suggest that the proposed algorithm represents the target posterior much more accurately and provides significantly smaller Monte Carlo error when compared to particle filtering.

Learning emotional speech by using Dirichlet Process Mixtures

Our aim in this paper is to illustrate the effectiveness of the Dirichlet Process Mixture (DPM) model for emotional speech class density estimation when the number of Gauss mixture components are unknown. The problem is modeled as a two-class classification problem where the classes are anger and-no-anger. Performance of the algorithm is evaluated on the features extracted from the emotion dataset EMO-DB, it is observed that the prior information inclusion led to increased non-anger recall rate. The introduced feature set performs perceptual analysis in time, spectral and Bark domains based on the Perceptual Evaluation of Audio Quality (PEAQ) model as described by the standard, ITU-R BS.1387-1 which provides a mathematical model resembling the human auditory system. Unlike the existing systems, the proposed feature set learns statistical characteristic of emotional differences hence enables us to represent the statistics of emotional audio with a small number of features.

Target tracking with regularized variable rate particle filters

Recently introduced variable rate particle filters (VRPF), utilizing semi-Markov models for maneuvering target tracking obtained superior performance compared to the standard Markov models [1]. However, degeneracy problem commonly encountered in particle filtering also arises in VRPF algorithm. In this work, regularization technique used in standard particle filtering [2] is integrated to VRPF modelling and regularized variable rate particle filters (R-VRPF) are introduced as a solution to degeneracy problem. Performance of proposed R-VRPF algorithm is investigated in a bearing only target tracking problem and it is shown that RMS position error is reduced due to the better approximation to the posterior distribution representing target position.