Sample Identification in Hip Hop Music

Sampling is a creative tool in composition that is widespread in popular music production and composition since the 1980's. However, the concept of sampling has for a long time been unaddressed in Music Information Retrieval. We argue that information on the origin of samples has a great musicological value and can be used to organise and disclose large music collections. In this paper we introduce the problem of automatic sample identification and present a first approach for the case of hip hop music. In particular, we modify and optimize an existing fingerprinting approach to meet the necessary requirements of a realworld sample identification task. The obtained results show the viability of such an approach, and open new avenues for research, especially with regard to inferring artist influences and detecting musical reuse. © 2013 Springer-Verlag.This research was done between Jan. and Sept. 2011 at the Music Technology Group of Universitat Pompeu Fabra in Barcelona, Spain. The authors would like to thank Perfecto Herrera and Xavier Serra for their advice and support. JS acknowledges JAEDOC069/2010 from Consejo Superior de Investigaciones Cient cas and 2009-SGR-1434 from Generalitat de Catalunya. MH acknowledges FP7-ICT-2011.1.5-287711.Peer Reviewe

Feature-based classification for audio bootlegs detection

n/

Detecting translingual plagiarism and the backlash against translation plagiarists

Os métodos de detecção de plágio registaram melhorias significativas ao longo das últimas décadas e, decorrente da investigação avançada realizada por linguistas computacionais e, sobretudo, por linguistas forenses, é, agora, maisfácil identiVcar estratégias de reutilização de texto simples e soVsticadas. Especificamente, simples algoritmos de comparação de texto criados por linguistas computacionais permitem detectar fácil e (semi-)automaticamente plágio literal,ipsis verbis (i.e. que consiste na reutilização de trechos de texto idênticos em diferentes documentos) como é o caso do Turnitin ou o SafeAssign , embora o desempenho destes métodos tenha tendência a piorar quando a reutilizaçãoé disfarçada através da introdução de alterações ao texto original. Neste caso, são necessárias técnicas linguísticas mais soVsticadas, como a análise de sobreposição lexical (Johnson, 1997), para detectar a reutilização. Contudo, estastécnicas são de aplicação muito limitada em casos de plágio translingue, em que determinado texto é traduzido e reutilizado sem atribuição da autoria ao texto original, proveniente de outra língua. Considerando que (a) normalmente,a tradução amadora (e.g. tradução literal ou tradução automática gratuita) é ométodo utilizado para plagiar; (b) é comum os plagiadores fazerem alterações aotexto, nomeadamente gramaticais e sintácticas, sobretudo após a tradução automática;e (c) os elementos lexicais são aqueles que a tradução automática processamais correctamente, antes da sua reutilização no texto derivado, este artigopropõe um método de detecção de plágio translingue informado pelas teorias datradução e da interlíngua (Selinker, 1972; Bassnett and Lefevere, 1998), bem comopelo princípio de singularidade linguística (Coulthard, 2004). Recorrendo a dadosempíricos do corpus CorRUPT (Corpus of Reused and Plagiarised Texts),um corpus de textos académicos e não académicos reais, que foram investigadose acusados de plagiar textos originais noutras línguas, demonstra-se a utilidadeda metodologia proposta para a detecção de plágio translingue. Finalmente,discute-se possíveis aplicações deste método como ferramenta de investigação emcontextos forenses.Plagiarism detection methods have improved signiVcantly over thelast decades, and as a result of the advanced research conducted by computationaland mostly forensic linguists, simple and sophisticated textual borrowingstrategies can now be identiVed more easily. In particular, simple text comparisonalgorithms developed by computational linguists allow literal, word-for-wordplagiarism (i.e. where identical strings of text are reused across diUerent documents)to be easily detected (semi-)automatically (e.g. Turnitin or SafeAssign),although these methods tend to perform less well when the borrowing is obfuscatedby introducing edits to the original text. In this case, more sophisticatedlinguistic techniques, such as an analysis of lexical overlap (Johnson, 1997), arerequired to detect the borrowing. However, these have limited applicability incases of translingual plagiarism, where a text is translated and borrowed withoutacknowledgment from an original in another language. Considering that(a) traditionally non-professional translation (e.g. literal or free machine translation)is the method used to plagiarise; (b) the plagiarist usually edits the textfor grammar and syntax, especially when machine-translated; and (c) lexicalitems are those that tend to be translated more correctly, and carried over to thederivative text, this paper proposes a method for translingual plagiarism detectionthat is grounded on translation and interlanguage theories (Selinker, 1972;Bassnett and Lefevere, 1998), as well as on the principle of linguistic uniqueness(Coulthard, 2004). Empirical evidence from the CorRUPT corpus (Corpus ofReused and Plagiarised Texts), a corpus of real academic and non-academic textsthat were investigated and accused of plagiarising originals in other languages, isused to illustrate the applicability of the methodology proposed for translingualplagiarism detection. Finally, applications of the method as an investigative toolin forensic contexts are discussed

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

General Undergraduate Catalog, 2017-2018

Marshall University Undergraduate Course Catalog for the 2017-2018 academic year.https://mds.marshall.edu/catalog_2010-2019/1002/thumbnail.jp

General Undergraduate Catalog, 2018-2019

Marshall University Undergraduate Course Catalog for the 2018-2019 academic year.https://mds.marshall.edu/catalog_2010-2019/1001/thumbnail.jp

General Undergraduate Catalog, 2019-2020

Marshall University Undergraduate Course Catalog for the 2019-2020 academic year.https://mds.marshall.edu/catalog_2010-2019/1000/thumbnail.jp

Catalog | 2018-2019 (May)

Vol. 107, No. 1 (May 2018). In its early years as the State Normal School, JSU produced a variety of publications (announcements, bulletins, and catalogs) that contain course information combined with the types of information that would later be found in yearbooks. Examples include historical information about the school, lists of enrolled students and club officers, photographs of athletic teams and literary clubs, notes on alumni, faculty and campus facilities, and more.https://digitalcommons.jsu.edu/lib_ac_bul_bulletin/1217/thumbnail.jp