Direitos autorais e música: tecnologia, direito e regulação

A partir de abordagem multidisciplinar, o artigo analisou o presente impacto da tecnologia sobre o Direito de Propriedade Intelectual, com ênfase nos fonogramas. Considerou a essencialidade dos recursos tecnológicos para permitir a universalização do acesso à música bem como para viabilizar a preservação dos direitos autorais. Cotejou o papel regulador do Direito no tema pesquisado e as ferramentas tecnológicas que podem facilitar a identificação da autoria e a justa partilha dos resultados econômicos da obra. Resultante de projeto de pesquisa envolvendo distintos programas de pós-graduação em Direito do país, foi elaborado a partir de análise teórica e dogmática com base no método lógico-dedutivo, transportando conhecimentos da área tecnológica, de forma a facilitar a compreensão dos aspectos jurídicos e econômicos relacionados ao direito autoral de fonogramas, utilizando-se de referenciais teóricos da Ciência da Computação, do Direito de Propriedade e do Direito Econômico. A técnica de pesquisa aplicada foi documentação indireta por meio de pesquisa bibliográfica, documental e legislativa. O artigo concluiu que a tecnologia pode ser empregada como uma importante aliada na busca por soluções que compatibilizem o Direito de Propriedade Intelectual relacionado à música em seus aspectos individual e social

Indexació de continguts televisius : anàlisi de so

Aquest és un projecte sobre la indexació de continguts televisius; és a dir, el procés d'etiquetatge de programes televisius per facilitar cerques segons diferents paràmetres. El món de la televisió està immers en un procés d'evolució i canvis gràcies a l'entrada de la televisió digital. Aquesta nova forma d'entendre la televisió obrirà un gran ventall de possibilitats i permetrà la interacció entre usuaris i emissora. El primer pas de la gestió de continguts consisteix en la indexació dels programes segons el contingut. Aquest és el nostre objectiu. Indexar els continguts televisius de manera automàtica mitjançant la intelligència artificial.Este proyecto trata sobre la indexación de contenidos televisivos; es decir, el proceso de etiquetaje de programas televisivos para facilitar búsquedas según diversos parámetros. El mundo de la televisión se encuentra inmerso en un proceso de evolución y cambios debido a la entrada de la televisión digital. Esta nueva forma de entender la televisión abrirá un gran abanico de posibilidades, permitiendo una interacción entre usuarios i emisora. El primer paso de esta gestión de contenidos consiste en la indexación de los programas según su contenido. Éste es nuestro objetivo. Indexar los contenidos televisivos de manera automática mediante la inteligencia artificial.This project is about TV content indexing; that is to say, the TV program labeling process to make searching easier according to different parameters. TV world is now immersed in an evolution and changing process due to the appearance of the digital TV. This new way of understanding the television will open a new set of possibilities, allowing an interaction between users and digital video broadcasters. The first step in this process of content management consists in program indexing according to its content. This is our main objective. Indexing TV contents automatically using the Artificial Intelligence

The Challenge of Balancing Competing Fundamental Rights in Online Enforcement of Copyright: A Study on Copyright in the Digital Single Market Directive

With the increasing use of the internet and online platforms, online copyright infringement has become a significant issue for the rightholders, platforms and governments worldwide. To tackle this issue, different jurisdictions adopted different approaches to online copyright enforcement, such as legislative-led, private-led or a combination of these government and voluntary systems, voluntary reactive systems being the most commonly used. However, as the platforms evolved and their services changed from the time that the legislation, such as Digital Millennium Copyright Act in the US and E-Commerce Directive in the EU, a growing need for up-to-date rules that can keep up with the technology has arisen. This triggered the policy reform actions in the EU, which resulted in the Directive on Copyright in the Digital Single Market (CDSMD) in 2019. However, the compatibility of the Directive’s “best efforts” requirements in Article 17 with fundamental rights, namely with Articles 7, 8, 11, 16 and 47 of the Charter of Fundamental Rights of the European Union (Charter), as well as Articles 6, 8 and 10 of European Convention on Human Rights (ECHR), constitutes the most significant concern regarding the new regime that the CDSMD introduces. The purpose of this study is two-fold: Firstly, to critically assess to what extent would the implementation of Article 17 of the CDSMD be compatible with users' right to privacy, data protection, freedom of information and an effective remedy and a fair trial under the Charter and the ECHR; as well as online content-sharing service providers’ (OCSSPs) freedom to conduct a business under Article 16 of the Charter. Secondly, if Article 17 were to violate the Charter and Convention, to suggest and appraise a number of procedural safeguards and possible amendments to ensure Article 17 compatibility with Articles 7, 8, 11, 16 and 47 of the Charter, as well as Articles 6, 8 and 10 of ECHR. Thus, this study examines the incompatibilities of Article 17’s obligations and critically examines the safeguards introduced by the CDSMD to suggest recommendations and procedural safeguards for the national implementations that would ensure the Article’s interference with aforementioned fundamental rights is limited and, therefore, its implementation is fundamental right-compliant

MUSIC IDENTIFICATION WITH WEIGHTED FINITE-STATE TRANSDUCERS

Music identification is the process of matching an audio stream to a particular song. Previous work has relied on hashing, where an exact or almost-exact match between local features of the test and reference recordings is required. In this work we present a new approach to music identification based on finite-state transducers and Gaussian mixture models. We apply an unsupervised training process to learn an inventory of music phone units similar to phonemes in speech. We also learn a unique sequence of music units characterizing each song. We further propose a novel application of transducers for recognition of music phone sequences. Preliminary experiments demonstrate an identification accuracy of 99.5 % on a database of over 15,000 songs running faster than real time. Index Terms — Music identification, acoustic modeling, finite-state transducers