Query by Example of Speaker Audio Signals using Power Spectrum and MFCCs

Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Meta-Regression Estimates for CGE Models: A Case Study for Input Substitution Elasticities in Production Agriculture

The selection of appropriate parameters for computable general equilibrium (CGE) models critically affects the results of applied economic modeling exercises. Valid and reliable parameter selection models are needed, and typically comprise direct estimation, expert opinion, or copycatting of results from seminal studies. The purpose of this study is to use meta-analysis to summarize and more accurately estimate elasticities of input substitution, specifically between labor and other inputs in agricultural production. We construct a comprehensive database of elasticity estimates through an extensive literature review, and perform a meta-regression analysis to identify structural sources of variation in elasticity estimates sampled from primary studies. The use of meta-analysis contributes to improved baseline analysis in CGE simulations because it allows for the computation of input parameters tailored to a specific CGE model setup. We correct for variations in research design, which are typically constant within studies, and account for bias associated with undue selection effects associated with editorial publication decision processes. Improved accuracy and knowledge of the distribution of imputed input parameters derived from a meta-analysis contributes to improved performance of CGE sensitivity analyses.meta-analysis, cross-price elasticity, input substitu¬tion, agricultural production, CGE parameters, Demand and Price Analysis, C13, C68, Q13,

A Transdisciplinary Approach to Construct Search and Integration

Human behaviors play a leading role in many critical areas including the adoption of information systems, prevention of many diseases, and educational achievement. There has been explosive growth of research in the behavioral sciences during the past decade. Behavioral science researchers are now recognizing that due to this ever expanding volume of research it is impossible to find and incorporate all appropriate inter-related disciplinary knowledge. Unfortunately, due to inconsistent language and construct proliferation across disciplines, this excellent but disconnected research has not been utilized fully or effectively to address problems of human health or other areas. This paper introduces a newly developed, cutting edge technology, the Inter-Nomological Network (INN) which for the first time provides an integrating tool to behavioral scientists so they may effectively build upon prior research. We expect INN to provide the first step in moving the behavioral sciences into an era of integrated science. INN is based on Latent Semantic Analysis (LSA), a theory of language use with associated automatic computerized text analysis capabilities

Developing a Model for Explaining Network Attributes and Relationships of Organised Crime Activities by Utilizing Network Science

The main objective of this research is to provide an innovative exploratory model for investigating substantive organised crime activities. The study articulates 30 critical independent variables related to organised crime, network science and a comprehensive exploratory approach which converts measurements of the variables into meaningful crime related inferences and conclusions. A case study was conducted to review initial feasibility of the selected variables, exploratory approach and model, and the results suggesting good effectiveness and useability

Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance