13 research outputs found

    Speech and crosstalk detection in multichannel audio

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    The analysis of scenarios in which a number of microphones record the activity of speakers, such as in a round-table meeting, presents a number of computational challenges. For example, if each participant wears a microphone, speech from both the microphone's wearer (local speech) and from other participants (crosstalk) is received. The recorded audio can be broadly classified in four ways: local speech, crosstalk plus local speech, crosstalk alone and silence. We describe two experiments related to the automatic classification of audio into these four classes. The first experiment attempted to optimize a set of acoustic features for use with a Gaussian mixture model (GMM) classifier. A large set of potential acoustic features were considered, some of which have been employed in previous studies. The best-performing features were found to be kurtosis, "fundamentalness," and cross-correlation metrics. The second experiment used these features to train an ergodic hidden Markov model classifier. Tests performed on a large corpus of recorded meetings show classification accuracies of up to 96%, and automatic speech recognition performance close to that obtained using ground truth segmentation

    Citizens' observatories for situation awareness in flooding

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    Citizens' observatories are emerging as a means to establish interaction and co-participation between citizens and authorities during both emergencies and the day-to-day management of fundamental resources. In this paper we present a case study in which a model of citizens' observatories is being been translated into practice in the WeSenseIt project. The WeSenseIt citizens' observatory provides a unique way of engaging the public in the decision-making processes associated with water and flood management through a set of new digital technologies. The WeSenseIt citizens' observatory model is being implemented in three case studies based in the UK, the Netherlands and Italy. We describe the findings and our experiences following preliminary evaluations of the technologies and the model of co-participation and describe our future research plans

    A Computational Model of Auditory Selective Attention

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    An overview of semantic search evaluation initiatives

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    Recent work on searching the Semantic Web has yielded a wide range of approaches with respect to the underlying search mechanisms, results management and presentation, and style of input. Each approach impacts upon the quality of the information retrieved and the user’s experience of the search process. However, despite the wealth of experience accumulated from evaluating Information Retrieval (IR) systems, the evaluation of Semantic Web search systems has largely been developed in isolation from mainstream IR evaluation with a far less unified approach to the design of evaluation activities. This has led to slow progress and low interest when compared to other established evaluation series, such as TREC for IR or OAEI for Ontology Matching. In this paper, we review existing approaches to IR evaluation and analyse evaluation activities for Semantic Web search systems. Through a discussion of these, we identify their weaknesses and highlight the future need for a more comprehensive evaluation framework that addresses current limitations

    Effective and efficient entity search in RDF data

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    Triple stores have long provided RDF storage as well as data access using expressive, formal query languages such as SPARQL. The new end users of the Semantic Web, however, are mostly unaware of SPARQL and overwhelmingly prefer imprecise, informal keyword queries for searching over data. At the same time, the amount of data on the Semantic Web is approaching the limits of the architectures that provide support for the full expressivity of SPARQL. These factors combined have led to an increased interest in semantic search, i.e. access to RDF data using Information Retrieval methods. In this work, we propose a method for effective and efficient entity search over RDF data. We describe an adaptation of the BM25F ranking function for RDF data, and demonstrate that it outperforms other state-of-the-art methods in ranking RDF resources. We also propose a set of new index structures for efficient retrieval and ranking of results. We implement these results using the open-source MG4J framework. © 2011 Springer-Verlag
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