Empirical Musicology Review
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Published By The Ohio State University Libraries

1559-5749, 1559-5749

2021 ◽  
Vol 16 (1) ◽  
pp. 34-46
Author(s):  
Mark R. H. Gotham

While it is encouraging to see renewed attention to 'openness' in academia, that debate (and its interpretation of the F.A.I.R. principles) is often rather narrowly defined. This paper addresses openness in a broad sense, asking not so much whether a project is open, but how open and to whom. I illustrate these ideas through examples of my own ongoing projects which to seek to make the most of a potential symbiosis between academic and wider musical communities. Specifically, I discuss how these communities can both benefit from – and even work together on building – highly accessible and interoperable corpora of scores and analyses when ambitious openness is factored into decision making from the outset.


2021 ◽  
Vol 16 (1) ◽  
pp. 47-64
Author(s):  
Alex Hofmann ◽  
Tomasz Miksa ◽  
Peter Knees ◽  
Asztrik Bakos ◽  
Hande Sağlam ◽  
...  

Recordings of musical practices are kept in various public institutions and private depositories around the world. They constitute valuable data for ethnomusicological research and are substantial for the world's musical heritage. At the moment, there are no commonly used systems and standards for organizing, describing or categorizing these data, which makes their use difficult. In this paper, we discuss the required steps to make them findable, accessible, interoperable and reusable (FAIR), and outline action items to reach these goals. We show solutions that help researchers to manage their data over the whole research lifecycle and discuss the benefits of combining technologies from information science, music information retrieval, and linked data, with the aim of giving incentives for the ethnomusicology research community to actively participate in these developments in the future.


2021 ◽  
Vol 16 (1) ◽  
pp. 134-144
Author(s):  
Omer Raz ◽  
Dror Chawin ◽  
Uri B. Rom
Keyword(s):  

This report documents a dataset consisting of expert annotations (symbolic data) of interthematic (higher-level) cadences in the exposition sections of all of Mozart's instrumental sonata-allegro movements.


2021 ◽  
Vol 16 (1) ◽  
pp. 1-4
Author(s):  
Fabian C. Moss ◽  
Markus Neuwirth
Keyword(s):  

No abstract available.


2021 ◽  
Vol 16 (1) ◽  
pp. 99-105
Author(s):  
Anna Aljanaki ◽  
Stefano Kalonaris ◽  
Gianluca Micchi ◽  
Eric Nichols

We present Multitrack Contrapuntal Music Archive (MCMA, available at https://mcma.readthedocs.io), a symbolic dataset of pieces specifically curated to comprise, for any given polyphonic work, independent voices. So far, MCMA consists only of pieces from the Baroque repertoire but we aim to extend it to other contrapuntal music. MCMA is FAIR-compliant and it is geared towards musicological tasks such as (computational) analysis or education, as it brings to the fore contrapuntal interactions by explicit and independent representation. Furthermore, it affords for a more apt usage of recent advances in the field of natural language processing (e.g., neural machine translation). For example, MCMA can be particularly useful in the context of language-based machine learning models for music generation. Despite its current modest size, we believe MCMA to be an important addition to online contrapuntal music databases, and we thus open it to contributions from the wider community, in the hope that MCMA can continue to grow beyond our efforts. In this article, we provide the rationale for this corpus, suggest possible use cases, offer an overview of the compiling process (data sourcing and processing), and present a brief statistical analysis of the corpus at the time of writing. Finally, future work that we endeavor to undertake is discussed.


2021 ◽  
Vol 16 (1) ◽  
pp. 16-33
Author(s):  
David M. Weigl ◽  
Tim Crawford ◽  
Aggelos Gkiokas ◽  
Werner Goebl ◽  
Emilia Gómez ◽  
...  

Vast amounts of publicly licensed classical music resources are housed within many different repositories on the Web encompassing richly diverse facets of information—including bibliographical and biographical data, digitized images of music notation, music score encodings, audiovisual performance recordings, derived feature data, scholarly commentaries, and listener reactions. While these varied perspectives ought to contribute to greater holistic understanding of the music objects under consideration, in practice, such repositories are typically minimally connected. The TROMPA project aims to improve this situation by interconnecting and enriching public-domain music repositories. This is achieved, on the one hand, by the application of automated, cutting-edge Music Information Retrieval techniques, and on the other, by the development of contribution mechanisms enabling users to integrate their expertise. Information within established repositories is interrelated with data generated by the project within a data infrastructure whose design is guided by the FAIR principles of data management and stewardship: making music information Findable, Accessible, Interoperable, and Reusable. We provide an overview of challenges of description, identification, representation, contribution, and reliability toward applying the FAIR principles to music information, and outline TROMPA's implementational approach to overcoming these challenges. This approach applies a graph-based data infrastructure to interrelate information hosted in different repositories on the Web within a unifying data model (a 'knowledge graph'). Connections are generated across different representations of music content beyond the catalogue level, for instance connecting note elements within score encodings to corresponding moments in performance time-lines. Contributions of user data are supported via privacy-first mechanisms that retain control of such data with the contributing user. Provenance information is captured throughout, supporting reproducibility and re-use of the data both within and outside the context of the project.


2021 ◽  
Vol 16 (1) ◽  
pp. 151-153
Author(s):  
Stefan Münnich

In this commentary on Hofmann et al. (2021), the notion of ethnomusicology and some of its underlying biases are questioned and reflected in the light of applying FAIR data principles to musicological research data from outside a Western canon and its musical practices.


2021 ◽  
Vol 16 (1) ◽  
pp. 145-150
Author(s):  
Lindsay Warrenburg

A corpus of Previously-Used Musical Stimuli (PUMS) is presented. The PUMS database is an online, publicly-available database where researchers can find a list of 22,417 musical stimuli that have been previously used in the literature on how music can convey or evoke emotions in listeners. A total of 306 studies on music and emotion are included in the database. Each musical stimulus used in these studies was coded according to various criteria: its designated emotion and how it was operationalized, its length, whether it is an excerpt from a longer work, and its style or genre. In the PUMS corpus, there is also information regarding the familiarity of the original participants with each musical sample, as well as information regarding whether each passage was used in a study about perceived or induced emotion. The name of the passage, composer, track number, and specific measure numbers or track location were noted when they were identified in the original paper. The database offers insight into how music has been used in psychological studies over a period of 90 years and provides a resource for scholars wishing to use music in future behavioral or psychophysical research. The PUMS database can be accessed online at https://osf.io/p4ta9.


2021 ◽  
Vol 16 (1) ◽  
pp. 85-98
Author(s):  
Ajay Srinivasamurthy ◽  
Sankalp Gulati ◽  
Rafael Caro Repetto ◽  
Xavier Serra

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.


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