Launching a denial of service (DoS) attack is trivial, but detection and response is a painfully slow and often a manual process. Automatic classification of attacks as singleor multi-source can help focus a response, but current packet-header-based approaches are susceptible to spoofing. This paper introduces a framework for classifying DoS attacks based on header content, transient ramp-up be...

Deconvolution of the speech excitation (source) and vocal tract (filter) components through log-magnitude spectral processing is well-established and has led to the well-known cepstral features used in a multitude of speech processing tasks. This paper presents a novel source-filter decomposition based on processing in the phase domain. We show that separation between source and filter in the l...

In independent component analysis (ICA) an instantaneous mix of sources can be recovered using maximum Likelihood (ML). In Convolutive blind source separation (BSS) the mixture arises as a combination of di erently convolved source signals due to time delays and a reverberating acoustic environment. Instead of modeling a particular time instant now a time window of the mixed signals has to be m...

Estimating the directions-of-arrival (DOAs) of propagating plane waves is a problem of interest in a variety of applications including radar, mobile communications, sonar, and seismology. The widely studied uniform linear array (ULA) can only provide estimates of source bearings relative to the array axis. A planar. array is required if estimates of source azimuth and elevation are required (2D...

The blind source separation problem is to extract the underlying source signals from a set of linear mixtures, where the mixing matrix is unknown. This situation is common in acoustics, radio, medical signal and image processing, hyperspectral imaging, and other areas. We suggest a two-stage separation process: a priori selection of a possibly overcomplete signal dictionary (for instance, a wav...

Source localization with a wireless sensor network remains an important area of research as the number of applications with this problem increases. This work considers the problem of source localization by a network of passive wireless sensors. The primary means by which localization is achieved is through direction-finding at each sensor, and in some cases, range estimation as well. Both singl...

This paper summarizes the audio part of the 2011 communitybased Signal Separation Evaluation Campaign (SiSEC2011). Four speech and music datasets were contributed, including datasets recorded in noisy or dynamic environments and a subset of the SiSEC2010 datasets. The participants addressed one or more tasks out of four source separation tasks, and the results for each task were evaluated using...

This paper introduces a Bayesian framework to detect multiple signals embedded in noisy observations from a sensor array. For various states of knowledge on the communication channel and the noise at the receiving sensors, a marginalization procedure based on recent tools of finite random matrix theory, in conjunction with the maximum entropy principle, is used to compute the hypothesis selecti...

⎯A semi-blind source extraction algorithm for noisy mixtures based on a linear predictor is proposed. The novel algorithm is firstly validated using the benchmark data and then is applied to extract array radar signal suffering from jamming interference. The results showed that the novel algorithm can effectively extract the target signal in radar echo. Thus, it has practical application prospe...

Source number estimation methods for single channel signal have been investigated and the improvements for each method are suggested in this work. Firstly, the single channel data is converted to multi-channel form by delay process. Then, algorithms used in the array signal processing, such as Gerschgorin's disk estimation (GDE) and minimum description length (MDL), are introduced to estimate t...