Dominic K. C. Ho

Simple design of oversampled uniform DFT filter banks with applications to subband acoustic echo cancellation

Abstract Subband adaptive filtering, which is the basis of modern acoustic echo cancellation (AEC) systems, is an important application of filter banks in which critical sampling cannot be used in general because of decimation aliasing effects. This leads to the use of oversampling schemes in the filter bank design wherein the perfect-reconstruction (PR) or near PR property is still required. In this work, a simple design technique for uniform DFT filter bank with near PR property is presented for the purpose of subband adaptive filtering. The prototype filter in the proposed filter banks is derived simply by performing an interpolation of a two-channel QMF filter, which can be obtained easily by computation or table look-up. An efficient implementation of the filter banks based on a weighted-overlap-add structure is described that allows flexibility in oversampling. The filter bank design technique presented in this paper is of particular interest in engineering applications, as demonstrated by design examples and experimental results in a real-time subband AEC application.

Feature analysis for the NIITEK ground-penetrating radar using order-weighted averaging operators for landmine detection

An automated methodology for combining Ground Penetrating Radar features from different depths is presented and analyzed. GPR data from the NIITEK system are processed by a depth-dependent, adaptive whitening algorithm. Shape and contrast features, including compactness, solidity, eccentricity, and relative area are computed at the different depths. These features must be combined to make a decision as to the presence of a landmine at a specific location. Since many of the depths contain no useful information and the depths of the mines are unknown, a strategy based on sorting is used. In a previous work, sorted features were combined via a rule-based system. In the current paper, an automated algorithm that builds a decision rule from sets of Ordered Weighted Average (OWA) operators is described. The OWA operator sorts the feature values, weights them, and performs a weighted sum of the sorted values, resulting in a nonlinear combination of the feature values. The weights of the OWA operators are trained off-line in combination with those of a decision-making network and held fixed during testing. The combination of OWA operators and decision-making network is called a FOWA network. The FOWA network is compared to the rule-based method on real data taken from multiple collections at two outdoor test sites.

Context-dependent fusion for landmine detection with ground-penetrating radar

We present a novel method for fusing the results of multiple landmine detection algorithms that use different types of features and different classification methods. The proposed fusion method, called Context-Dependent Fusion (CDF) is motivated by the fact that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth. The training part of CDF has two components: context extraction and algorithm fusion. In context extraction, the features used by the different algorithms are combined and used to partition the feature space into groups of similar signatures, or contexts. The algorithm fusion component assigns an aggregation weight to each detector in each context based on its relative performance within the context. Results on large and diverse Ground Penetrating Radar data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our initial experiments have also indicated that the context-dependent fusion outperforms all individual detectors.

Hierarchical Methods for Landmine Detection with Wideband Electro-Magnetic Induction and Ground Penetrating Radar Multi-Sensor Systems

A variety of algorithms are presented and employed in a hierarchical fashion to discriminate both anti-tank (AT) and anti-personnel (AP) landmines using data collected from wideband electromagnetic induction (WEMI) and ground penetrating radar (GPR) sensors mounted on a robotic platform. The two new algorithms for WEMI are based on the In-phase vs. Quadrature plot (the Argand diagram) of the complex measurement obtained at a single spatial location. The angle prototype match method uses the sequence of angles as a feature vector. Prototypes are constructed from these feature vectors and used to assign mine confidence to a test sample. The angle model based KNN method uses a two parameter model; where the parameters are fit to the In-phase and Quadrature data. For the GPR data, the Linear Prediction Processing and Spectral Features are calculated. All four features from WEMI and GPR are used in a Hierarchical Mixture of Experts model to increase the landmine detection rate. The EM algorithm is used to estimate the parameters of the hierarchical mixture. Instead of a two way mine/non-mine decision, the HME structure is trained to make a five way decision which aids in the detection of the low metal anti personnel mines.

Efficient Joint Source and Sensor Localization in Closed-Form

This letter considers the problem of simultaneously locating multiple disjoint sources and refining erroneous sensor positions using TDOA measurements. The previous work by Yang and Ho to solve this problem cannot provide optimum accuracy for the sensor positions. The proposed estimator improves the previous method so that both the source and the sensor position estimates can achieve the Cramer-Rao lower bound (CRLB) accuracy. The theoretical derivation is corroborated by simulations.

Wavelet based speech presence probability estimator for speech enhancement

A reliable speech presence probability (SPP) estimator is important to many frequency domain speech enhancement algorithms. It is known that a good estimate of SPP can be obtained by having a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. Recently, the wavelet denoising with multitaper spectrum (MTS) estimation technique was suggested for such purpose. However, traditional approaches directly make use of the wavelet shrinkage denoiser which has not been fully optimized for denoising the MTS of noisy speech signals. In this paper, we firstly propose a two-stage wavelet denoising algorithm for estimating the speech power spectrum. First, we apply the wavelet transform to the periodogram of a noisy speech signal. Using the resulting wavelet coefficients, an oracle is developed to indicate the approximate locations of the noise floor in the periodogram. Second, we make use of the oracle developed in stage 1 to selectively remove the wavelet coefficients of the noise floor in the log MTS of the noisy speech. The wavelet coefficients that remained are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. To adapt to the enhanced a-posteriori SNR function, we further propose a new method to estimate the generalized likelihood ratio (GLR), which is an essential parameter for SPP estimation. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables an improvement in both the quality and intelligibility of the enhanced speeches.

Nest: Networked smartphones for target localization

This paper presents Nest, a novel system using wirelessly connected smartphones to localize remote targets based on sound and image inputs. The system has four major components: image-based localization, acoustics-based localization, wireless ad-hoc networking, and middle-ware services for time synchronization and secure communication. Single-image, two-image, and TDOA-acoustics based methods have been developed and a prototype system has been implemented on Google Nexus One smartphones running Android. Experimental results show that the localization accuracies of the single-image-based and two-image-based method are around 94.6% and up to 92.1%, respectively. The localization errors of the acoustics-based method are within 80 centimeters.

Region Processing of Ground Penetrating Radar and Electromagnetic Induction for Handheld Landmine Detection

An analysis of the utility of region-based processing of Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI) is presented. Algorithms for re-sampling GPR data acquired over non-rectangular and non-regular grids are presented. Depth-dependent whitening is used to form GPR images as functions of depth bins. Shape, size, and contrast-based features are used to distinguish mines from non-mines. The processing is compared to point-wise processing of the same data. Comparisons are made to GPR data collected by machine and by humans. Evaluations are performed on calibration data, for which the ground truth is known to the algorithm developers, and blind data, for which the ground truth is not known to the algorithm developers.

Detecting landmines using weighted density distribution function features

Land mine detection using metal detector (MD) and ground penetrating radar (GPR) sensors in hand-held units is a difficult problem. Detection difficulties arise due to: 1) the varying composition and type of metal in land mines, 2) the time-varying nature of background and 3) the variation in height and velocity of the hand-held unit in data measurement. This research introduces new spatially distributed MD features for differentiating land mine signatures from background. The spatially distributed features involve correlating sequences of MD energy values with six weighted density distribution functions. These features are evaluated using a standard back propagation neural network on real data sets containing more than 2,300 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions.

Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data

A possibilistic K-Nearest Neighbors classifier is presented to classify mine and non-mine objects using data collected from a wideband electromagnetic induction (WEMI) sensor. The proposed classifier is motivated by the observation that buried objects often have consistent signatures depending on their metal content, size, shape, and depth. Given a joint orthogonal matching pursuits (JOMP) sparse representation, particular target types consistently selected the same dictionary elements. The proposed classifier distinguishes between target types using the frequency of dictionary elements selected by potential landmine alarms. Results are shown on data containing sixteen landmine types and several non-mine examples.