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TECHNOLOGY ENHANCED INTERACTION FRAMEWORK : ISSUES IN EVALUATING A NEW SOFTWARE DESIGN FRAMEWORK AND METHOD

Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, ELECTRONICS AND COMMUNICATION
Author(s): KEWALIN ANGKANANON , LESTER GILBERT

Abstract: A Technology Enhanced Interaction Framework has been developed to support designers and developers designing and developing technology enhanced interactions for complex scenarios involving disabled people. A literature review showed that while there have been many studies concerned with methods for evaluating software designs few studies addressed ways to evaluate software design methods. Issues of motivation, time, and understanding when validating and evaluating the Technology Enhanced Interaction framework were identified through a literature review and questionnaires and interviews with experts. The advantages and disadvantages of a range of experimental design approaches to sourcing scenarios, gathering requirements and designing solutions were considered. Future work will consist of the implementation of a motivating user evaluation approach involving both self-evaluations by designers and expert evaluations that learns the lessons from others’ experiences of software method eval

  • Publication Date: 13-Oct-2013
  • DOI: 10.15224/978-981-07-7965-8-05
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PERFORMING VENUE INFORMATION RETRIEVAL FROM RECORDED SOUNDTRACKS-AN ACOUSTIC FEATURE EXTRACTION APPROACH

Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, ELECTRONICS AND COMMUNICATION
Author(s): FRANCIS F. LI

Abstract: Keywords and descriptors are important metadata for multimedia content. Such metadata are associated with the programme, and are generated to facilitate indexing, search, clustering, archiving, semantic analysis and many other potential applications. Information about performing or recording venues provides a useful cue for content search and authentication, but is difficult to obtain form the media content. This paper proposes to determine the recording venues from extracted room acoustic features contained in the soundtracks. Room acoustic decay curves are obtained via maximum likelihood estimation from recording. The decay curve is a statistical description of the impulse response of a room, and provides a good discriminator for recording venues. Machine learning is then performed on the estimated decay curves to make the decision. This paper presents the rationale of the method, describes the algorithms and validates the method by simulations.

  • Publication Date: 13-Oct-2013
  • DOI: 10.15224/978-981-07-7965-8-07
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