Challenges to Digital Musicology
Challenges to Digital Musicology: An Annotated Bibliography by Jessica Holmes
Abdallah, S., Benetos, E., Gold, N., Hargreaves, S., Weyde, T., & Wolff, D. (2017). The Digital Music Lab: A Big Data Infrastructure for Digital Musicology. Journal on Computing and Cultural Heritage (JOCCH), 10, 2.
Abdallas et al., discusses the framework and infrastructure necessaryto build a functional and integrated digital music software system that is capable of both musical research and analysis. Proponents behind the DML system demonstrate thorough understanding of front and back end technology, human computer interaction, user behavior, big data, data analysis, and the complex nature of audio and visual data within integrated software formats. The DML proposal proves to be forward looking as it presents a way to work through past challenges and promotes the use of an open framework for linked data.
Casey, M. A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., & Slaney, M. (2008). Content-based music information retrieval: Current directions and future challenges. Proceedings of the IEEE, 96(4), 668-696.
The authors describe the challenges brought into music information retrieval (MIR) from the view of music research experts. The authors take into consideration musical elements such as identification, copyright, melody, performance, music notation, and symbolic representation. They demonstrate how each of these elements contributes to the complex nature of music research and retrieval as an interdisciplinary process and how each element is crucial in the methodology behind musical analysis. The paper also establishes a structure for how MIR systems should be evaluated; a necessary component from which system design must be based. The paper concludes with an analysis on how challenges such as content based searching, tool integration, and polyphonic music must be developed with a user preference and focus.
Gracy, K. F., Zeng, M. L., & Skirvin, L. (2013). Exploring methods to improve access to Music resources by aligning library Data with Linked Data: A report of methodologies and preliminary findings. Journal of the American Society for Information Science and Technology, 64(10), 2078-2099.
Gracy, et all were part of a project that sought to understand how library data could be connected to CKAN’s Linked Data Hub in regards to music data. Identifying existing music related datasets and connected them to metadata elements. Then, library bibliographic data taken and mapped to the same elements until the data sets and bibliographic data had a unified set of elements. This project is useful in understanding how to creating systems of Linked Data between broad data sets and specialized data sets. It also highlights that these types of projects and research must be done now because each one is going to uncover new and previously hidden challenges and how we can make data records more useable from a library perspective.
Kulik, E. (2010). Digital Musical Libraries: The Patterns of Use of Digital Musical Scores. Fontes Artis Musicae, 65-75.
Kulik’s research studied patron use of musical scores and how the usage contributed to the organization of music information. Through the use of both quantitative reports and surveys to various music libraries, Kulik pieces together a puzzle of how musical data is organized through the means of format. The author also found that his research alluded to other challenges in digital musicology such as the way that musicians want their musical data displayed, different digital library structure and formats, and identifying access points in music information retrieval.
Lee, J. H., & Cunningham, S. J. (2013). Toward an understanding of the history and impact of user studies in music information retrieval. Journal of Intelligent Information Systems, 41(3), 499-521.
Lee and Cunningham executed an intense study of information published on the topic of music information retrieval in order to gather a current state of digital musicology. Their analysis uncovered several found challenges to digital musicology. These include but are not limited too user/music generalization, lack of standards for interpreting study results, too little existing systematic synthesis research results, and a lack of partnership between system designers and user behavior with music retrieval. The analysis on the existing literature of MIR is extremely helpful in helping researchers understand the history behind the field and gives those interested ideas in which they should move forward in regards to digital music retrieval and processing.
Oramas, S., & Sordo, M. (2016). Knowledge Is Out There: A New Step in the Evolution of Music Digital Libraries. Fontes Artis Musicae, 63(4), 285-298.
Oramas gives us a technical perspective of the evolution of digital music libraries and encourages the development of the Semantic Web and of knowledge extraction as the key to the future of digital musicology. Oramas methodologies a means to extract knowledge, build a knowledge base, graph the data, and then exploit this graph in other applications to support digital musicology. Oramas exemplifies how one might extract information from text documents to build a knowledge graph and data set and then applies the data set to artist biographies. This methodology is useful in figuring out how to best extract knowledge to create interconnected sets of data that would be useful in digital music libraries.
Pablo Bello, J., & Underwood, K. (2012). Improving access to digital music through content-based analysis. OCLC Systems & Services: International digital library perspectives, 28(1), 17-31.
Pablo Bello and Underwood evaluate a variety of music information retrieval theories to describe and understand the current state of music retrieval methods in place. The evaluative jargon used by the authors helps those who may not be music research professionals understand the implications, challenges, and benefits of text-based, content-based and chord based retrieval methods. The authors also discuss the future of structural-based musical analysis and how repetition of audio patterns can be computerized and generate useable data that will be a useful tool in all communities.
Pugin, L. (2015). The challenge of data in digital musicology. Frontiers in Digital Humanities, 2, 4.
The lack of access in high quality datasets for musicology material remains to be one of the biggest challenges in digital musicology. Pugin discusses the various initiatives that have attempted to spearhead content based data for digital musicology and how these projects have answered many questions and challenges but discovered far more new issues. The lack of technology that exists, including OMR, is the biggest obstacle in tackling these issues. Pugin urges musicology researchers to continue to make the case for preserving their content digitally and working to create tools and software that will help advances these projects.
Wang, C., Li, J., & Shi, S. (2006). The design and implementation of a digital music library. International Journal on Digital Libraries, 6(1), 82-97.
This paper discusses the framework, data, and query languages used in the Harbin Institute of Technology’s Digital Music Library. The HIT-DML is a digital music library that was developed on a database system and is therefore one of the leading DML projects worldwide. The HIT-DML uses quantitative data mining techniques to evaluate and report on how the system is functioning and processes music metadata. The authors also discuss how HIT-DML processes data distribution and structural storage for music metadata information. HIT-DML is a working project that is continuously evaluated and updated as more information and tools emerges on how to make database and multimedia technologies work together.
Waters, J., & Allen, R. B. (2010). Music metadata in a new key: Metadata and annotation for music in a digital world. Journal of Library Metadata, 10(4), 238-256.
This article attempts to dissect the way usage of metadata for recorded music. Waters and Allen discuss various approaches to semantic annotation and some of the software tools that have been created to enhance music researchers ability to record audio data. The authors are one of the few who comment on documenting the experience of music rather than the historical or contextual importance of a piece. Documenting experiences is a challenge in any venue, but particularly with music, it is something that each individual experiences differently. Therefore, how does the music research community document musical experience as it relates to the population? And how do they make those experiences available with systematic descriptions?
Bibliography
Abdallah, S., Benetos, E., Gold, N., Hargreaves, S., Weyde, T., & Wolff, D. (2017). The Digital Music Lab: A Big Data Infrastructure for Digital Musicology. Journal on Computing and Cultural Heritage (JOCCH), 10(1), 2.
Casey, M. A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., & Slaney, M. (2008). Content-based music information retrieval: Current directions and future challenges. Proceedings of the IEEE, 96(4), 668-696.
Gracy, K. F., Zeng, M. L., & Skirvin, L. (2013). Exploring methods to improve access to Music resources by aligning library Data with Linked Data: A report of methodologies and preliminary findings. Journal of the American Society for Information Science and Technology, 64(10), 2078-2099.
Kulik, E. (2010). Digital Musical Libraries: The Patterns of Use of Digital Musical Scores. Fontes Artis Musicae, 65-75.
Lee, J. H., & Cunningham, S. J. (2013). Toward an understanding of the history and impact of user studies in music information retrieval. Journal of Intelligent Information Systems, 41(3), 499-521.
Oramas, S., & Sordo, M. (2016). Knowledge Is Out There: A New Step in the Evolution of Music Digital Libraries. Fontes Artis Musicae, 63(4), 285-298.
Pablo Bello, J., & Underwood, K. (2012). Improving access to digital music through content-based analysis. OCLC Systems & Services: International digital library perspectives, 28(1), 17-31.
Pugin, L. (2015). The challenge of data in digital musicology. Frontiers in Digital Humanities, 2, 4.
Wang, C., Li, J., & Shi, S. (2006). The design and implementation of a digital music library. International Journal on Digital Libraries, 6(1), 82-97.
Waters, J., & Allen, R. B. (2010). Music metadata in a new key: Metadata and annotation for music in a digital world. Journal of Library Metadata, 10(4), 238-256.
