Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin

Computational Modelling and Analysis of Vibrato and Portamento in Expressive Music Performance

PhD, 148ppVibrato and portamento constitute two expressive devices involving continuous pitch modulation and is widely employed in string, voice, wind music instrument performance. Automatic extraction and analysis of such expressive features form some of the most important aspects of music performance research and represents an under-explored area in music information retrieval. This thesis aims to provide computational and scalable solutions for the automatic extraction and analysis of performed vibratos and portamenti. Applications of the technologies include music learning, musicological analysis, music information retrieval (summarisation, similarity assessment), and music expression synthesis. To automatically detect vibratos and estimate their parameters, we propose a novel method based on the Filter Diagonalisation Method (FDM). The FDM remains robust over short time frames, allowing frame sizes to be set at values small enough to accurately identify local vibrato characteristics and pinpoint vibrato boundaries. For the determining of vibrato presence, we test two alternate decision mechanisms—the Decision Tree and Bayes’ Rule. The FDM systems are compared to state-of-the-art techniques and obtains the best results. The FDM’s vibrato rate accuracies are above 92.5%, and the vibrato extent accuracies are about 85%. We use the Hidden Markov Model (HMM) with Gaussian Mixture Model (GMM) to detect portamento existence. Upon extracting the portamenti, we propose a Logistic Model for describing portamento parameters. The Logistic Model has the lowest root mean squared error and the highest adjusted Rsquared value comparing to regression models employing Polynomial and Gaussian functions, and the Fourier Series. The vibrato and portamento detection and analysis methods are implemented in AVA, an interactive tool for automated detection, analysis, and visualisation of vibrato and portamento. Using the system, we perform crosscultural analyses of vibrato and portamento differences between erhu and violin performance styles, and between typical male or female roles in Beijing opera singing

Flamenco music information retrieval.

El flamenco, un género musical centrado en la improvisación y la espontaneidad, tiene su origen en el sur de España y atrae a una creciente comunidad de aficionados de países de todo el mundo. El aumento constante y la accesibilidad a colecciones digitales de flamenco, en archivos de música y plataformas online, exige el desarrollo de métodos de análisis y descripción computacionales con el fin de indexar y analizar el contenido musical de manera automática. Music Information Retrieval (MIR) es un área de investigación multidisciplinaria dedicada a la extracción automática de información musical desde grabaciones de audio y partituras. Sin embargo, la gran mayoría de las herramientas existentes se dirigen a la música clásica y la música popular occidental y, a menudo, no se generalizan bien a las tradiciones musicales no occidentales, particularmente cuando las suposiciones relacionadas con la teoría musical no son válidas para estos géneros. Por otro lado, las características y los conceptos musicales específicos de una tradición musical pueden implicar nuevos desafíos computacionales, para los cuales no existen métodos adecuados. Esta tesis enfoca estas limitaciones existentes en el área abordando varios desafíos computacionales que surgen en el contexto de la música flamenca. Con este fin, se realizan una serie de contribuciones en forma de algoritmos novedosos, evaluaciones comparativas y estudios basados en datos, dirigidos a varias dimensiones musicales y que abarcan varias subáreas de ingeniería, matemática computacional, estadística, optimización y musicología computacional. Una particularidad del género, que influye enormemente en el trabajo presentado en esta tesis, es la ausencia de partituras para el cante flamenco. En consecuencia, los métodos computacionales deben basarse únicamente en el análisis de grabaciones, o de transcripciones extraídas automáticamente, lo que genera una colección de nuevos problemas computacionales. Un aspecto clave del flamenco es la presencia de patrones melódicos recurrentes, que esán sujetos a variación y ornamentación durante su interpretación. Desde la perspectiva computacional, identificamos tres tareas relacionadas a esta característica que se abordan en esta tesis: la clasificación por melodía, la búsqueda de secuencias melódicas y la extracción de patrones melódicos. Además, nos acercamos a la tarea de la detección no supervisada de frases melódicas repetidas y exploramos el uso de métodos de deep learning para la identificación de cantaores en grabaciones de video y la segmentación estructural de grabaciones de audio. Finalmente, demostramos en un estudio de minería de datos, cómo una exploración de anotaciones extraídas de manera automática de un corpus amplio de grabaciones nos ayuda a descubrir correlaciones interesantes y asimilar conocimientos sobre este género mayormente indocumentado.Flamenco is a rich performance-oriented art music genre from Southern Spain, which attracts a growing community of aficionados around the globe. The constantly increasing number of digitally available flamenco recordings in music archives, video sharing platforms and online music services calls for the development of genre-specific description and analysis methods, capable of automatically indexing and examining these collections in a content-driven manner. Music Information Retrieval is a multi-disciplinary research area dedicated to the automatic extraction of musical information from audio recordings and scores. Most existing approaches were however developed in the context of popular or classical music and do often not generalise well to non-Western music traditions, in particular when the underlying music theoretical assumptions do not hold for these genres. The specific characteristics and concepts of a music tradition can furthermore imply newcomputational challenges, for which no suitable methods exist. This thesis addresses these current shortcomings of Music Information Retrieval by tackling several computational challenge which arise in the context of flamenco music. To this end, a number of contributions to the field are made in form of novel algorithms, comparative evaluations and data-driven studies, directed at various musical dimensions and encompassing several sub-areas of computer science, computational mathematics, statistics, optimisation and computational musicology. A particularity of flamenco, which immensely shapes the work presented in this thesis, is the absence of written scores. Consequently, computational approaches can solely rely on the direct analysis of raw audio recordings or automatically extracted transcriptions, and this restriction generates set of new computational challenges. A key aspect of flamenco is the presence of reoccurring melodic templates, which are subject to heavy variation during performance. From a computational perspective, we identify three tasks related to this characteristic - melody classification, melody retrieval and melodic template extraction - which are addressed in this thesis. We furthermore approach the task of detecting repeated sung phrases in an unsupervised manner and explore the use of deep learning methods for image-based singer identification in flamenco videos and structural segmentation of flamenco recordings. Finally, we demonstrate in a data-driven corpus study, how automatic annotations can be mined to discover interesting correlations and gain insights into a largely undocumented genre

Identification of potential Music Information Retrieval technologies for computer-aided jingju singing training

Comunicació presentada a: 5th China Conference on Sound and Music Technology - Chinese Traditional Music Technology Session celebrada el 21 de novembre de 2017 a Suzhou, Xina.Music Information Retrieval (MIR) technologies have been proven useful in assisting western classical singing training. Jingju (also known as Beijing or Peking opera) singing is different from western singing in terms of most of the perceptual dimensions, and the trainees are taught by using mouth/heart method. In this paper, we first present the training method used in the professional jingju training classroom scenario and show the potential benefits of introducing the MIR technologies into the training process. The main part of this paper dedicates to identify the potential MIR technologies for jingju singing training. To this intent, we answer the question: how the jingju singing tutors and trainees value the importance of each jingju musical dimension—intonation, rhythm, loudness, tone quality and pronunciation? This is done by (i) classifying the classroom singing practices, tutor's verbal feedbacks into these 5 dimensions, (ii) surveying the trainees. Then, with the help of the music signal analysis, a finer inspection on the classroom practice recording examples reveals the detailed elements in the training process. Finally, based on the above analysis, several potential MIR technologies are identified and would be useful for the jingju singing training.This by the European Research Council under the European Union’s Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583)

Mapping Global Theatre Histories

Tanks to all at Palgrave Macmillan who encouraged and shaped this project, especially Nicola Cattini, Tomas Rene, Vicky Bates, and the anonymous readers of the proposal. Tanks to the colleagues who gave me insights, including Dean Adams, Allison Amidei, Bruce Auerbach, Hala Baki, Tomas Burch, Carlos Cruz, Kaja Dunn, David Fillmore, Andrew Hartley, Jorge Huerta, Rick Kemp, Chuyun Oh, Kaustavi Sarkar, Dylan Savage, Joanne Tompkins, Robin Witt, Amanda Zhou, and members of the “Pedagogy of Extraordinary Bodies” working group at the American Society for Teatre Research conference in fall 2017. Tanks also to Chuyun Oh and Kaustavi Sarkar for help with illustrations here. And thanks to the authors and editors of Wikipedia, who have made many details of theatre history quickly accessible online, with further references given as well. Tanks to the colleagues who responded to my e-mail query in summer 2017 about a potential theatre history textbook, especially Sarah Bay-Cheng, Cheryl Black, Sara Ellen Brady, David Carlyon, Teresa DurbinAmes, Susan Kattwinkel, Maiya Murphy, John O’Connor, Felicia Ruf, Shannon Blake Skelton, and Nathan Tomas. Tanks to the artists I have met, who gave me insights about their work. Tese included Kazimierz Braun (who directed me in Te Card Index at the University of Notre Dame in 1982, welcomed my visit to his theatre in Poland, and co-wrote a play with me that he staged at Swarthmore College in 1986), Herbert Blau (my dissertation mentor, 1988–1992), Ola Rotimi (who lectured in one of my classes), William Sun (who discussed playwriting with me and introduced me to others), Richard Schechne

The performance of identity in Chinese popular music

Popular music in Chinese languages both reflects and influences how its audiences perceive themselves and their position in the world around them. This book analyses the role of popular music in identity formation through detailed comparisons of the pop star Faye Wong, the rock band Second Hand Rose and the electrofolk artist Xiao He, in five thematic chapters. Chapter 1, Place, follows the history of popular music through Shanghai, Hong Kong, Taipei and Beijing, concluding that language is defining. Chapter 2, Genre and Classification, argues that genre distinctions, and by extension class identities, are secondary to affiliations along region, gender, generation and marketability. The psycho-analytical approach of chapter 3, Sex, Gender, and Desire, explores how popular music reiterate and challenge stereotypes surrounding the passive beauty, coolness and brotherhood. Chapter 4, Theatricality, argues that theatrical performances negotiate the boundary between stage world and ordinary reality through make-believe and reflectiveness. Finally, chapter 5, Organizing Music, submits that music happens through reproduction, variation and selection, and in constant interaction with ecologies and collectives. In the end, this book itself strives to make these sounds, images and texts available for the incessant, piecemeal work of worldmaking.LEI Universiteit LeidenThe Hulsewé-Wazniewski Foundation for the advancement of teaching and research in the archeology, art and material culture of China at Leiden UniversityAsian Studie

Correction occurrence analysis spreadsheet

<p>The accompanying "Correction occurrence" spreadsheet for the paper:</p> <p>这个文件-“纠正事件”Excel表格是下面论文的附属文件:</p> <blockquote> <p>Identification of potential Music Information Retrieval technologies for computer-aided jingju singing training, Rong Gong, Xavier Serra, Chinese traditional music technology session - China conference on sound and music technology 2017, Suzhou, China</p> </blockquote> <p>Each occurrence is annotated with (1) teacher's feedback (2) signal analysis method (3) classified dimension or detailed elements.</p> <p>每一个纠正事件都标注有(1)老师的反馈,(2)所使用的信号分析方法,(3)分类的维度和细节元素。</p