A general framework for learning prosodic-enhanced representation of rap lyrics

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Learning and analyzing rap lyrics is a significant basis for many Web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web. Although numerous studies have explored the topic, knowledge in this field is far from satisfactory, because critical issues, such as prosodic information and its effective representation, as well as appropriate integration of various features, are usually ignored. In this paper, we propose a hierarchical attention variational a utoe ncoder framework (HAVAE), which simultaneously considers semantic and prosodic features for rap lyrics representation learning. Specifically, the representation of the prosodic features is encoded by phonetic transcriptions with a novel and effective strategy (i.e., rhyme2vec). Moreover, a feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation. A comprehensive empirical evaluation demonstrates that the proposed framework outperforms the state-of-the-art approaches under various metrics in different rap lyrics learning tasks

Categorización de letras de canciones de un portal web usando agrupación

Algoritmos de clasificación y de agrupación han sido usados ampliamente en sistemas de recuperación de información musical (MIR) para organizar repositorios musicales en categorías o grupos relacionados, por ejemplo género, modo o tema, usando el sonido o sonido en combinación con la letra de la canción. Sin embargo, la investigación relacionada con agrupación usando solamente la letra de la canción es poca. El objetivo principal de este trabajo es definir un modelo no supervisado de minería de datos para la agrupación de letras de canciones recopiladas en un portal web, usando solamente características de la letra de la canción, con el fin de ofrecer mejores opciones de búsqueda a los usuarios del portal. El modelo propuesto primero identifica el lenguaje de las letras de canciones usando Naive Bayes y n-grams (para el caso de este trabajo se identificaron 30.000 letras de canciones en Español y 30.000 en Ingles). Luego las letras son representadas en un modelo de espacio vectorial Bag OfWords (BOW), usando características de Part Of Speech (POS) y transformando los datos al formato TF-IDF. Posteriormente, se estima el numero apropiado de agrupaciones (K) y se usan algoritmos particionales y jerárquicos con el _n de obtener los grupos diferenciados de letras de canciones. Para evaluar los resultados de cada agrupación se usan medidas como el índice Davies Bouldin (DBI) y medidas internas y externas de similaridad de los grupos. Finalmente, los grupos se etiquetan usando palabras frecuentes y reglas de asociación identificadas en cada grupo. Los experimentos realizados muestran que la música puede ser organizada en grupos relacionados como género, modo, sentimientos y temas, la cual puede ser etiquetada con técnicas no supervisadas usando solamente la información de la letra de la canción.Abstract. Classification and clustering algorithms have been applied widely in Music Information Retrieval (MIR) to organize music repositories in categories or clusters, like genre, mood or topic, using sound or sound with lyrics. However, clustering related research using lyrics information only is not much. The main goal of this work is to define an unsupervised text mining model for grouping lyrics compiled in a website, using lyrics features only, in order to offer better search options to the website users. The proposal model first performs a language identification for lyrics using Nafive Bayes and n-grams (for this work 30.000 lyrics in Spanish and 30.000 in English were identifed). Next lyrics are represented in a vector space model Bag Of Words (BOW), using Part Of Speech (POS) features and transforming data to TF-IDF format. Then, the appropriate number of clusters (K) is estimated and partitional and hierarchical methods are used to perform clustering. For evaluating the clustering results, some measures are used such as Davies Bouldin Index (DBI), intra similarity and inter similarity measures. At last, the final clusters are tagged using top words and association rules per group. Experiments show that music could be organized in related groups as genre, mood, sentiment and topic, and tagged with unsupervised techniques using only lyrics information.Maestrí

Exploring Shakespeare's Sonnets with SPARSAR

Shakespeare's Sonnets have been studied by literary critics for centuries after their publication. However, only recently studies made on the basis of computational analyses and quantitative evaluations have started to appear and they are not many. In our exploration of the Sonnets we have used the output of SPARSAR which allows a full-fledged linguistic analysis which is structured at three macro levels, a Phonetic Relational Level where phonetic and phonological features are highlighted; a Poetic Relational Level that accounts for a poetic devices, i.e. rhyming and metrical structure; and a Syntactic-Semantic Relational Level that shows semantic and pragmatic relations in the poem. In a previous paper we discussed how colours may be used appropriately to account for the overall underlying mood and attitude expressed in the poem, whether directed to sadness or to happiness. This has been done following traditional approaches which assume that the underlying feeling of a poem is strictly related to the sounds conveyed by the words besides/beyond their meaning. In that study we used part of Shakespeare's Sonnets. We have now extended the analysis to the whole collection of 154 sonnets, gathering further evidence of the colour-sound-mood relation. We have also extended the semantic-pragmatic analysis to verify hypotheses put forward by other quantitative computationally-based analysis and compare that with our own. In this case, the aim is trying to discover what features of a poem characterize most popular sonnets

Bertsobot: gizaki-robot arteko komunikazio eta elkarrekintzarako portaerak

216 p.Bertsobot: Robot-Portaerak Gizaki-Robot Arteko Komunikazio eta ElkarrekintzanBertsotan aritzeko gaitasuna erakutsiko duen robot autonomoa garatzeada gure ikerketa-lanaren helburu behinena. Bere egitekoa, bertsoa osatzekoinstrukzioak ahoz jaso, hauek prozesatu eta ahalik eta bertsorik egokienaosatu eta kantatzea litzateke, bertsolarien oholtza gaineko adierazkortasunmaila erakutsiz gorputzarekin. Robot-bertsolariak, gizaki eta roboten artekoelkarrekintza eta komunikazioan aurrera egiteko modua jarri nahi luke, lengoaianaturala erabiliz robot-gizaki arteko bi noranzkoko komunikazioan

MUSICA E COGNIZIONE: DIFFERENZE INDIVIDUALI CORRELATE ALL'EXPERTISE MUSICALE

Da alcuni decenni chi pratica musica (suonando uno strumento o canta) è diventato oggetto di attenzione per la ricerca in psicologia sperimentale. Ciò è dovuto al fatto che i musicisti sono un esempio concreto di come l'expertise in una specifica area di attività promuova la plasticità cerebrale con associati benefici dal punto di vista cognitivo. Ad oggi tale interesse è ricaduto anche su una specifica categoria di artisti: i freestylers (rap). Nel presente studio sono stati reclutati musicisti, non musicisti e freestylers ai quali sono stati somministrati i seguenti compiti e questionari: music span, digit span, spatial span (randomizzati), matrici progressive di Raven, un compito di memoria verbale presente all'interno della scala WAIS-IV, un compito di n-back, il miniPROMS questionnaire, GOLD-MSI questionnaire, eBMRQ questionnaire, Big Five Inventory 2 e l'Hollingshead Four Factor Index. L'obiettivo ultimo è quello di dimostrare e rafforzare le evidenze a favore di un vantaggio in compiti di memoria a breve termine/di lavoro per i musicisti (e presumibilmente anche per i freestylers) rispetto ai non musicisti. Sono stati inoltre presi in considerazioni alcuni possibili fattori mediatori come tratti di personalità e status socio-economico

Ensimmäinen ja toinen käsikirjoitusversio väitöskirjaa varten

This publication contains the first and the second manuscript version for LauriLahti’s doctoral dissertation in 2015 "Computer-assisted learning based on cumulative vocabularies, conceptual networks and Wikipedia linkage".Tämä julkaisu sisältää ensimmäisen ja toisen käsikirjoitusversion Lauri Lahden väitöskirjaan vuonna 2015 "Tietokoneavusteinen oppiminen perustuen karttuviin sanastoihin, käsiteverkostoihin ja Wikipedian linkitykseen".Not reviewe