We introduce two large open data collections of Indian Art Music, both its Carnatic and Hindustani traditions, comprising audio from vocal concerts, editorial metadata, and time-aligned melody, rhythm, and structure annotations. Shared under Creative Commons licenses, they currently form the largest annotated data collections available for computational analysis of Indian Art Music. The collections are intended to provide audio and ground truth for several music information research tasks and large-scale data-driven analysis in musicological studies. A part of the Saraga Carnatic collection also has multitrack recordings, making it a valuable collection for research on melody extraction, source separation, automatic mixing, and performance analysis. We describe the tenets and the process of collection, annotation, and organization of the data. We provide easy access to the audio, metadata, and the annotations in the collections through an API, along with a companion website that has example scripts to facilitate access and use of the data. To sustain and grow the collections, we provide a mechanism for both the research and music community to contribute additional data and annotations to the collections. We also present applications with the collections for music education, understanding, exploration, and discovery.
e data-driven computational research on automatic jingju (also known as Beijing or Peking opera) singing evaluation lacks a suitable and comprehensive a cappella singing audio dataset. In this work, we present an a cappella singing audio dataset which consists of 120 arias, accounting for 1265 melodic lines. is dataset is also an extension our existing CompMusic jingju corpus. Both professional and amateur singers were invited to the dataset recording sessions, and the most common jingju musical elements have been covered. is dataset is also accompanied by metadata per aria and melodic line annotated for automatic singing evaluation research purpose. All the gathered data is openly available online 1 .
When lyrics of tonal languages are set to music, the pitch contour of the tones has to agree to a certain extent with the melodic contour to assure intelligibility. e relationship between the linguistic tones of the complex dialectal construct used in jingju (commonly known as Beijing or Peking opera) and its melody has been largely studied, but not de nite consensus has been achieved among scholars. A er reviewing the related literature, we present a rst approach for the quantitative analysis of the relationship between linguistic tones and melody in jingju using a collection of machine readable music scores with tone category annotations for 7,283 syllables. We describe two statistical analyses performed in this collection regarding the melodic contour for each syllable and the pitch height relationship in 5,494 pairs of consecutive syllables. We argue that the obtained results contribute to supporting claims from the literature and complementing others, although some limitations of the approach might nuance the con dence of their validity.
Here we present a computational approach to identifying melodic patterns in a dataset of 145 MusicXML scores with the aim of contributing to centonization theory in the Moroccan tradition of Arab-Andalusian Music -a theory in development by expert performer and researcher of this tradition, Amin Chaachoo. Central to his work is the definition of a set of characteristic patterns, or centos, for each t . ab', or melodic mode. We apply three methods: TF-IDF, Maximally General Distinctive Patterns (MGDP) and the Structure Induction Algorithm (SIA) to identify characteristic patterns at the level of t . ab'. A substantial number of the centos proposed by Chaachoo are identified and new melodic patterns are retrieved. A discussion with Chaachoo about the obtained results promoted the elicitation of other categories of recurrent patterns in the tradition different from the centos, contributing to a deeper musicological knowledge of the tradition.
In the medieval Islamic territories of the Iberian Peninsula known as Al-Andalus a unique style of music was formed combining local practices with Arab sensibilities. After the fall of the last Andalusian kingdom, this classical repertoire has been preserved to the present in North African countries. The idiosyncrasies of this repertoire, which combines musical traits from Western and Eastern Mediterranean traditions in orchestral and choral settings, as well as instrumental and vocal solos, deserves an in depth musicological study, that can benefit from computational tools for corpusdriven research. On the other hand, the characteristics of this music poses interesting challenges to MIR methods and therefore offer new research opportunities to this field. To address these topics, we present here the first complete release of the corpus for the research of the Moroccan tradition of Arab-Andalusian music built in the framework of the CompMusic project. The corpus comprises three data collections, namely audio recordings, music scores and lyrics, as well as related annotations and metadata. We also present a series of Jupyter Notebooks for browsing and retrieving data from the corpus. Both the corpus and notebooks are completely open to the research community.
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