The Directional Dependence of the Lunar Cherenkov Technique for UHE Neutrino Detection

The LUNASKA (Lunar UHE Neutrino Astrophysics with the Square Kilometre Array) project is a theoretical and experimental project developing the lunar Cherenkov technique for the next generation of giant radio-telescope arrays. This contribution presents our simulation results on the directional dependence of the technique for UHE neutrino detection. In particular, these indicate that both the instantaneous sensitivities and time-integrated limits from lunar Cherenkov experiments such as those at Parkes, Goldstone, Kalyazin and ATCA are highly anisotropic. We study the regions of the sky which have not been probed by either these or other experiments, and present the expected sky coverage of future experiments with the SKA. Our results show how the sensitivity of Lunar Cherenkov observations to potential astrophysical sources of UHE particles may be maximised by choosing appropriate observations dates and antenna-beam pointing positions.Comment: 4 pages, 4 figures, presented at ARENA 2008, Rome, Ital

Automatic extraction of rules for sentence boundary disambiguation

ABSTRACT Transformation-based learning (TBL) is the most important machine learning theory aiming at the automatic extraction of rules based on already tagged corpora. However, the application of this theory to a certain application without taking into account the features that characterize this application may cause problems regarding the training time cost as well as the accuracy of the extracted rules. In this paper we present a variation of the basic idea of the TBL and we apply it to the extraction of the sentence boundary disambiguation rules in real-world text, a prerequisite for the vast majority of the natural language processing applications. We show that our approach achieves considerably higher accuracy results and, moreover, requires minimal training time in comparison to the traditional TBL

A comparative study in automatic recognition of broadcast audio

This paper provides a thorough description of a methodology which leads to high accuracy as regards automatic analysis of broadcast audio. The main objective is to find a feature set for efficient speech/music discrimination while keeping the number of its dimensions as small as possible. Three groups of parameters based on Mel-scale filterbank, MPEG-7 standard and wavelet decomposition are examined in detail. We annotated on-line radio recordings characterized by great diversity, for building probabilistic models and testing four frameworks. The proposed approach utilizes wavelets and MPEG-7 ASP descriptor for modeling speech and music respectively, and results to 98.5 % average recognition rat

Autoregressive Time-Frequency Interpolation in the Context of Missing Data Theory for Impulsive Noise Compensation

The present paper reports on a novel technique for the identification and replacement of spectral coefficients degraded by impulsive noise. The problem is viewed from the perspective of Missing Feature Theory (MFT). The analysis is carried out in the linear spectrum prior to, or after applying the mel-scale filter-bank depending on whether one aims at improving the quality of perception of speech recordings or at Automatic Speech Recognition (ASR). Each filter-bank output is considered to be a time series drawn from an Auto-Regressive process (AR). A validation corpus of undistorted recordings is used to derive a-priori bounds on the expected prediction error of each AR model. In operational conditions, the prediction procedure is monitored and the violation of the statistical bounds indicates band corruption and entails the substitution of the degraded spectral coefficients by the prediction of the corresponding AR model. ASR experiments and informal listening tests demonstrate large improvement in terms of word recognition performance and Itakura-Saito divergence at very low Signal to Impulsive Noise Ratios (SINRs). Data, and implementation code can be found at

A multidomain approach for automatic home environmental sound classification

This article presents a multidomain approach which addresses the problem of automatic home environmental sound recognition. The proposed system will be part of a human activity monitoring system which will be based on heterogeneous sensors. This work concerns the audio classification component and its primary role is to detect anomalous sound events. We compare the discriminative capabilities of three feature sets (MFCC, MPEG-7 low level descriptors and a novel set based on wavelet packets) with respect to the classification of ten sound classes. These are combined with state of the art generative techniques (GMM and HMM) for estimating the density function of each class. The highest average recognition rate is 95.7% and is achieved by the vector formed by all the feature sets juxtaposed