41 research outputs found
Lowering Barriers to Entry: YouTube, Fair Use, and the Copyright Claims Board
Get PDFThe Internet has transformed the landscape of media production by opening the doors of creation to anyone with a computer and an idea. YouTube allows for millions of individuals to post and disseminate content at a low cost to widespread audiences. But while the barriers to entry for content creation have lowered, the barriers to the legal copyright system have remained largely unmoved since YouTube’s inception. This Note seeks to explore the exact specifications of YouTube’s copyright system, both the one mandated by law and the one created voluntarily by YouTube, in order to understand where fair use stands in online copyright infringement detection. Additionally, the Note proposes the newly functional Copyright Claims Board as a way of lowering the barrier to entry into the legal system to allow for content creators to fight against online copyright abuse
Learner-generated comic (lgc): a production model
Get PDFRecent advancement of authoring tools has fostered widespread interest towards using comics as a Digital Storytelling medium. This technology integrated learning approach is known as Learner-Generated Comic (LGC) production; where learners' knowledge and ideas on various subjects are synthesized in a form of digital
educational comic. Despite the prior evidences for the didactic values of LGC production, most scholars do not emphasise on a quality, theoretically supported, and strategic LGC production methodology that accommodate to interrelated key elements and production methods of LGC. As a result, there is a tendency to view LGC production as challenging and impractical. Essentially, there is a lack of conceptual models and methods that comprehensively tailor the crucial theories, elements, techniques, technological, and systematic processes of LGC production. Within this context, this study attempts to propose LGC production model that serves as systematic approach which includes the fundamental components for learners to produce digital educational comics. Therefore, in order to accomplish the main aim, a number of sub objectives are formed: (1) to determine the core components for LGC production model, (2) to construct a systematic LGC production model based on the identified components, (3) to evaluate the proposed LGC production model, and (4) to assess the LGC products developed by the proposed model users. This study adopts the Design Science Research methodology as the framework of the research process. Activities of LGC production model construction include literature review and comparative study, expert consultation, and user participation. The proposed model is evaluated through user experience testing and expert review. Results from hypothesis testing concludes that the
proposed LGC production model is significantly perceived as having quality in serving as a guideline for learners to design and develop digital educational comics. It was also found that the proposed model has been well-accepted by local and international experts. In addition, assessment of LGC products developed from the user experience testing has implicated there are significance differences between
LGC products developed by the proposed model users and non-users. In conclusion, adoption of a systematic, scholarly grounded, and authenticated LGC production model can contribute to the planning, implementation, and evaluation of Digital Storytelling session that enhance learning experience through LGC design and
development
Learning the meaning of music
Get PDFThesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 99-104).Expression as complex and personal as music is not adequately represented by the signal alone. We define and model meaning in music as the mapping between the acoustic signal and its contextual interpretation - the 'community metadata' based on popularity, description and personal reaction, collected from reviews, usage, and discussion. In this thesis we present a framework for capturing community metadata from free text sources, audio representations general enough to work across domains of music, and a machine learning framework for learning the relationship between the music signals and the contextual reaction iteratively at a large scale. Our work is evaluated and applied as semantic basis functions - meaning classifiers that are used to maximize semantic content in a perceptual signal. This process improves upon statistical methods of rank reduction as it aims to model a community's reaction to perception instead of relationships found in the signal alone. We show increased accuracy of common music retrieval tasks with audio projected through semantic basis functions. We also evaluate our models in a 'query-by-description' task for music, where we predict description and community interpretation of audio. These unbiased learning approaches show superior accuracy in music and multimedia intelligence tasks such as similarity, classification and recommendation.by Brian A. Whitman.Ph.D
Large-Scale Pattern Discovery in Music
Get PDFThis work focuses on extracting patterns in musical data from very large collections. The problem is split in two parts. First, we build such a large collection, the Million Song Dataset, to provide researchers access to commercial-size datasets. Second, we use this collection to study cover song recognition which involves finding harmonic patterns from audio features. Regarding the Million Song Dataset, we detail how we built the original collection from an online API, and how we encouraged other organizations to participate in the project. The result is the largest research dataset with heterogeneous sources of data available to music technology researchers. We demonstrate some of its potential and discuss the impact it already has on the field. On cover song recognition, we must revisit the existing literature since there are no publicly available results on a dataset of more than a few thousand entries. We present two solutions to tackle the problem, one using a hashing method, and one using a higher-level feature computed from the chromagram (dubbed the 2DFTM). We further investigate the 2DFTM since it has potential to be a relevant representation for any task involving audio harmonic content. Finally, we discuss the future of the dataset and the hope of seeing more work making use of the different sources of data that are linked in the Million Song Dataset. Regarding cover songs, we explain how this might be a first step towards defining a harmonic manifold of music, a space where harmonic similarities between songs would be more apparent
