Knowledge Organization Systems (KOS) in the Semantic Web: A Multi-Dimensional Review

Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009 a significant number of conventional knowledge organization systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses "LOD KOS" as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS).Comment: 31 pages, 12 figures, accepted paper in International Journal on Digital Librarie

Jazz Standards: From the Manuscript to Multiple Possibilities through Computation

Over the last few years, OMR has been developed and studied. New perspectives have been provided that include handwritten music. Nonetheless, most of the research produced focuses on neumatic, mensural and cultured music. Barely any examples of non-canonical and modern music can be found. This paper is a statement of intention of a larger research project, which will end in the presentation of a thesis. This thesis project provides an idea of how to include a musical genre such as Jazz in OMR studies, until now rejected, by adapting existing tools and procedures, and by examining the ways to carry it out and what the best approaches are. It will result in not only plain transcription but also in the analysis offered by a music that has not been studied in this way and so intensively

Logic-based Modelling of Musical Harmony for Automatic Characterisation and Classification

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorMusic like other online media is undergoing an information explosion. Massive online music stores such as the iTunes Store1 or Amazon MP32, and their counterparts, the streaming platforms, such as Spotify3, Rdio4 and Deezer5, offer more than 30 million6 pieces of music to their customers, that is to say anybody with a smart phone. Indeed these ubiquitous devices offer vast storage capacities and cloud-based apps that can cater any music request. As Paul Lamere puts it7: “we can now have a virtually endless supply of music in our pocket. The ‘bottomless iPod’ will have as big an effect on how we listen to music as the original iPod had back in 2001. But with millions of songs to chose from, we will need help finding music that we want to hear [...]. We will need new tools that help us manage our listening experience.” Retrieval, organisation, recommendation, annotation and characterisation of musical data is precisely what the Music Information Retrieval (MIR) community has been working on for at least 15 years (Byrd and Crawford, 2002). It is clear from its historical roots in practical fields such as Information Retrieval, Information Systems, Digital Resources and Digital Libraries but also from the publications presented at the first International Symposium on Music Information Retrieval in 2000 that MIR has been aiming to build tools to help people to navigate, explore and make sense of music collections (Downie et al., 2009). That also includes analytical tools to suppor

En busca de una perspectiva transdisciplinar, el reconocimiento óptico musical; los manuscritos en el proceso creativo del Jazz y las posibilidades de análisis de la edición digital

La hipótesis de partida es que las nuevas tecnologías permiten hacer un cambio de paradigma en el objeto y la metodología de estudio. La entrada al mundo digital en la musicología permite abordar cuestiones a las que anteriormente no se podía haber dado respuesta. El uso del OMR no corrompe el ritual de anotación tradicional del lápiz y papel, excluyendo lo digital fuera del proceso creativo y utilizando las tecnologías a posteriori para la conservación del objeto, su estudio, su análisis y su difusión. De este modo se mantienen la tradicionalidad artística y la potencialidad de las tecnologías que caracteriza las humanidades digitales

Volume 33, Number 3, September 2013

Digitized September 2013 issue of the OLAC Newsletter

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Music Encoding Conference Proceedings 2021, 19–22 July, 2021 University of Alicante (Spain): Onsite & Online

Este documento incluye los artículos y pósters presentados en el Music Encoding Conference 2021 realizado en Alicante entre el 19 y el 22 de julio de 2022.Funded by project Multiscore, MCIN/AEI/10.13039/50110001103