An Alternative Postulate to see Melody as “Language”

The paper proposes a way to see melodic features in music/songs in the terms of “letters” constituting “words”, while in return investigating the fulfillment of Zipf-Mandelbrot Law in them. Some interesting findings are reported including some possible conjectures for classification of melodic and musical artifacts considering several aspects of culture. The paper ends with some discussions related to further directions, be it enrichment in musicology and the possible plan for musical generative art

Features for the classification and clustering of music in symbolic format

Tese de mestrado, Engenharia Informática, Universidade de Lisboa, Faculdade de Ciências, 2008Este documento descreve o trabalho realizado no âmbito da disciplina de Projecto em Engenharia Informática do Mestrado em Engenharia Informática da Faculdade de Ciências da Universidade de Lisboa. Recuperação de Informação Musical é, hoje em dia, um ramo altamente activo de investigação e desenvolvimento na área de ciência da computação, e incide em diversos tópicos, incluindo a classificação musical por géneros. O trabalho apresentado centra-se na Classificação de Pistas e de Géneros de música armazenada usando o formato MIDI. Para resolver o problema da classificação de pistas MIDI, extraimos um conjunto de descritores que são usados para treinar um classificador implementado através de uma técnica de Máquinas de Aprendizagem, Redes Neuronais, com base nas notas, e durações destas, que descrevem cada faixa. As faixas são classificadas em seis categorias: Melody (Melodia), Harmony (Harmonia), Bass (Baixo) e Drums (Bateria). Para caracterizar o conteúdo musical de cada faixa, um vector de descritores numérico, normalmente conhecido como ”shallow structure description”, é extraído. Em seguida, eles são utilizados no classificador — Neural Network — que foi implementado no ambiente Matlab. Na Classificação por Géneros, duas propostas foram usadas: Modelação de Linguagem, na qual uma matriz de transição de probabilidades é criada para cada tipo de pista midi (Melodia, Harmonia, Baixo e Bateria) e também para cada género; e Redes Neuronais, em que um vector de descritores numéricos é extraído de cada pista, e é processado num Classificador baseado numa Rede Neuronal. Seis Colectâneas de Musica no formato Midi, de seis géneros diferentes, Blues, Country, Jazz, Metal, Punk e Rock, foram formadas para efectuar as experiências. Estes géneros foram escolhidos por partilharem os mesmos instrumentos, na sua maioria, como por exemplo, baixo, bateria, piano ou guitarra. Estes géneros também partilham algumas características entre si, para que a classificação não seja trivial, e para que a robustez dos classificadores seja testada. As experiências de Classificação de Pistas Midi, nas quais foram testados, numa primeira abordagem, todos os descritores, e numa segunda abordagem, os melhores descritores, mostrando que o uso de todos os descritores é uma abordagem errada, uma vez que existem descritores que confundem o classificador. Provou-se que a melhor maneira, neste contexto, de se classificar estas faixas MIDI é utilizar descritores cuidadosamente seleccionados. As experiências de Classificação por Géneros, mostraram que os Classificadores por Instrumentos (Single-Instrument) obtiveram os melhores resultados. Quatro géneros, Jazz, Country, Metal e Punk, obtiveram resultados de classificação com sucesso acima dos 80% O trabalho futuro inclui: algoritmos genéticos para a selecção de melhores descritores; estruturar pistas e musicas; fundir todos os classificadores desenvolvidos num único classificador.This document describes the work carried out under the discipline of Computing Engineering Project of the Computer Engineering Master, Sciences Faculty of the Lisbon University. Music Information Retrieval is, nowadays, a highly active branch of research and development in the computer science field, and focuses several topics, including music genre classification. The work presented in this paper focus on Track and Genre Classification of music stored using MIDI format, To address the problem of MIDI track classification, we extract a set of descriptors that are used to train a classifier implemented by a Neural Network, based on the pitch levels and durations that describe each track. Tracks are classified into four classes: Melody, Harmony, Bass and Drums. In order to characterize the musical content from each track, a vector of numeric descriptors, normally known as shallow structure description, is extracted. Then they are used as inputs for the classifier which was implemented in the Matlab environment. In the Genre Classification task, two approaches are used: Language Modeling, in which a transition probabilities matrix is created for each type of track (Melody, Harmony, Bass and Drums) and also for each genre; and an approach based on Neural Networks, where a vector of numeric descriptors is extracted from each track (Melody, Harmony, Bass and Drums) and fed to a Neural Network Classifier. Six MIDI Music Corpora were assembled for the experiments, from six different genres, Blues, Country, Jazz, Metal, Punk and Rock. These genres were selected because all of them have the same base instruments, such as bass, drums, piano or guitar. Also, the genres chosen share some characteristics between them, so that the classification isn’t trivial, and tests the classifiers robustness. Track Classification experiments using all descriptors and best descriptors were made, showing that using all descriptors is a wrong approach, as there are descriptors which confuse the classifier. Using carefully selected descriptors proved to be the best way to classify these MIDI tracks. Genre Classification experiments showed that the Single-Instrument Classifiers achieved the best results. Four genres achieved higher than 80% success rates: Jazz, Country, Metal and Punk. Future work includes: genetic algorithms; structurize tracks and songs; merge all presented classifiers into one full Automatic Genre Classification System

A New Way of Moving: Developing a Solo Drumset Practice Informed by Embodied Music Cognition

This research examines how insights drawn from the field of Embodied Music Cognition can be repurposed to instigate creative development within the practice of an improvising drummer. Following a process- driven practice-led research model, I correlate academic research to aspects of pedagogical and professional practice, generating original theoretical insight and embodied knowledge in two primary areas: first, I arrive at an understanding of sticking cells as embodied knowledge encoded with specific rhythmic forms; second, I develop an original taxonomy for classifying types of individual and combined movement cycles as applied to the drumset. I combine these two as variable parameters within an original generative process entitled somatic parameter layering; which I use to furnish musical outputs that are found within a series of original recorded works, embedded throughout this dissertation. Through analysis of these works, I identify five strategic implementations of somatic parameter layering: Hide/Reveal, Modulation Obfuscation, Unison/Interlace, Fragmentation, and Expansion/Contraction. I then repurpose the parameters of sticking cells and movement cycles into an analytical model for investigating drumset activity, which is tested on an excerpt drawn from a live performance by American jazz drummer Bill Stewart, revealing his manipulation of movement as a parameter for both idea generation and development. The creative works of this research are situated within a historically emergent community of Australian improvising musicians, whom I refer to as Antripodean improvisers. I present an outline of the key artists working in the idiom and provide analysis of representative works to build a profile of the improvisational logic underpinning their shared practice. I explain how the professional requirements of interacting with these musicians have provided a primary motivation for undertaking the research project

Plotting Poetry 3. Conference report

Plotting Poetry 3. Conference repor

Using text mining techniques for classical music scores analysis

Music Classification is a particular area of Computational Musicology that provides valuable insights about the evolving of compo- sition patterns and assists in catalogue generation. The proposed work detaches from former works by classifying music based on music score in- formation. Text Mining techniques support music score processing while Classification techniques are used in the construction of decision mod- els. Although research is still at its earliest beginnings, the work already provides valuable contributes to symbolic music representation process- ing and subsequent analysis. Score processing involved the counting of ascending and descending chromatic intervals, note duration and meta- information tagging. Analysis involved feature selection and the evalu- ation of several data mining algorithms, ensuring extensibility towards larger repositories or more complex problems. Experiments report the analysis of composition epochs on a subset of the Mutopia project open archive of classical LilyPond-annotated music scores

Literature as a methodology to teach English in Primary School: The Witches by Roald Dahl

The current high requirement for English language skills calls for an educational system that integrates the process of teaching and learning the language, leaving behind the so-called traditional methodologies or those that teach it exclusively based on writing and grammar. This project aims to show English teachers in Primary Education a more recent and practical methodological perspective of teaching the English language. It also seeks to encourage primary students in Spain to read in their second language (SL), i.e. English and to improve their SL in a different and more creative and fun way. In order to do so, a classroom intervention has been proposed based on the children’s fantasy novel The Witches by Roald Dahl (1983) and by practising a combination of three teaching methodologies, namely, Total Physical Response, The Communicative Approach and Project-based Learning.La demanda actual de conocimientos de inglés exige un sistema educativo que integre el proceso de enseñanza y aprendizaje del idioma, dejando atrás las denominadas metodologías tradicionales o las que lo enseñan exclusivamente basadas en la escritura y la gramática. El objetivo de este trabajo es mostrar a los profesores de inglés en Educación Primaria una metodología reciente y práctica para la enseñanza de la lengua inglesa. También pretende animar a los alumnos de Educación Primaria a leer en la segunda lengua de una forma diferente, creativa y divertida. Para ello se ha propuesto una intervención en el aula basada en la novela fantástica infantil Las Brujas de Roald Dahl (1983) y en la práctica de una combinación de tres metodologías de enseñanza, como son, la Respuesta Física Total, el Enfoque Comunicativo y el Aprendizaje basado en Proyectos.Grado en Educación Primari

Versification and Authorship Attribution

The technique known as contemporary stylometry uses different methods, including machine learning, to discover a poem’s author based on features like the frequencies of words and character n-grams. However, there is one potential textual fingerprint stylometry tends to ignore: versification, or the very making of language into verse. Using poetic texts in three different languages (Czech, German, and Spanish), Petr Plecháč asks whether versification features like rhythm patterns and types of rhyme can help determine authorship. He then tests its findings on two unsolved literary mysteries. In the first, Plecháč distinguishes the parts of the Elizabethan verse play The Two Noble Kinsmen written by William Shakespeare from those written by his coauthor, John Fletcher. In the second, he seeks to solve a case of suspected forgery: how authentic was a group of poems first published as the work of the nineteenth-century Russian author Gavriil Stepanovich Batenkov? This book of poetic investigation should appeal to literary sleuths the world over.illustrato

A STUDY OF MISSA ARIRANG BY COOL-JAE HUH: ELEMENTS OF KOREAN TRADITIONAL FOLK MUSIC

This document focuses on a setting of the Ordinary of the Mass, Missa Arirang, composed by Cool-Jae Huh (b. 1965). Huh is one of South Korea's most prolific and innovative composers. He is especially known for his use of Korean folk songs in Western musical settings. In the Missa Arirang, Huh employs six traditional folk songs, called Min-yo from different provinces. The musical idioms of each type of folk song vary considerably. This study explores general information about Korean folk songs and the specific use of elements of Korean folk music, such as modes, rhythmic patterns, and unique ornamentations, which appear in this piece. Arirang, the title of the Mass, is the most famous and celebrated folk song in the history of Korea. It has served as the basis for pieces in almost all musical genres. This document investigates Arirang's significance in understanding Korean culture, and introduces the four versions of Arirang adopted in Huh's work. Missa Arirang is not only a compelling choral composition, containing Korean traditional musical aspects, but also a beneficial resource to introduce traditional Korean music to non-Korean musicians. In addition, the piece delivers the message of "peace" through various musical contents in the composition