Bridging the Gap: Demonstrating the Applicability of Linguistic Analysis Tools in Digital Musicology (2023)

Abstract: This study introduces the novel concepts of Explicit and Implicit Musical Parameters (EMPs and IMPs) and demonstrates their application in digital musicology. Furthermore, it discusses the concept of ‘musical words’, that suggests representing explicit and implicit musical parameters as words or textual entities. This ‘music-to-text’ approach allows the application of advanced techniques and tools commonly used within the computational linguistics for the analysis of musical data, highlighting the structural parallels between music and language. Lastly, the findings of this paper not only illustrate the feasibility of this approach but also pave the way for further interdisciplinary studies and the advancement of analytical user-friendly tools that are applicable in both computational linguistics and digital musicology.

## introduction in the age of digital humanities, interdisciplinary dialogue between interrelated scientific fields is becoming increasingly important. with the intentions of showing the possible benefits of interdisciplinary approaches, this paper investigates to what extent easy-to-use off-the-shelf software-tools and methods commonly used within the computational linguistics (cl) can be adopted and applied by digital musicologists for their own research within their respective field. an initial assessment of the relevant scientific literature underscores the urgency and relevance of such an exploration: though the potential merits (as well the presumed deficiencies) of using computational methods in musicology have already been recognised and emphasized decades ago @xcite @xcite , data available on lens.org, a repository of worldwide patent and academic knowledge, reveals a significant and widening gap between the number of publications in digital musicology and those in computational linguistics (cf. figure 1 ). it is reasonable to assume that this discrepancy might not only persist but potentially grow wider, as with the growing public interest and economic relevance of natural language processing and generation technologies, such as the now famous chatgpt, computational linguistics is likely to gain even more resources, both in academia as well as the free market. however, the significance of this research paper lies in its potential to create a bridge between two seemingly disparate fields: successfully applying cl tools within the field of digital musicology could not only expand research possibilities drastically, but also facilitate a more holistic approach when researching and trying to understand the similarities between language and music. this study is structured into four main parts: an introduction that discusses structural parallels between music and language, further introducing the novel idea of implicit and explicit musical parameters (emps and imps); a data section describing the data preparation process, encompassing the collection, tokenization and transformation of data used for this study; a methodology section, in which will be explained, how gui-applications frequently used in computational linguistics, such as antconc, can be adapted to analyse textual music data; and finally, a demonstration section, in which methods are presented that showcase how antconc could be employed to address various musicological questions. ## data the second section of this paper will focus on data collecting and preparation. in this process several tools where utilized to convert musical source material into textual data, as necessitated by the tool antconc used for corpus analysis in sections 3 and 4 of this study (cf. figure 2 ). the complete process can be divided into two sub-sections: corpus creation as well as tokenization and database creation. ## methodology in the following section, the methodological approach, used for demonstrating the applicability of well-established cl tools for answering questions emanating from the field of musicology will be presented. 13 ## demonstration in this last section, three distinct ways to utilize antconc for music analysis will be demonstrated. each of these demonstrations will focus on a particular aspect of music analysis: these analyses will be performed on the custom database, created within antconc earlier in section 2.2. numerical representations of musical events will be treated as textual entities or 'musical words', allowing antconc to be used as an user-friendly tool for conducting various music analyses. note: as the pattern search will be performed not on absolute pitch values, but on values extracted from the relational parameter "pitchdif-ferencetonextpitch", patterns will be identified across melodic transpositions. ## conclusion this study has successfully demonstrated the practical applicability of well-established computational linguistics (cl) tools, such as antconc, in the the field of digital musicology. the successful reinterpretation of tokenized music data as 'musical words' outlined in sections 2 and 3 has not only indicated the existence of certain inherent structural/formal similarities between language and music; it also unveiled the potential benefits of developing new analytical tools and methods that can be used both within the fields of linguistics and musicology. a notable area for future code development lies in the optimization of the tokenization and parameter extraction processes. integrating more versatile tools like music21 into the tokenization process could address the current limitations encountered with miditok, particularly its restriction to the rather unreliable .mid-file format. utilizing mu-sic21's comprehensive capabilities would enable the processing of a broader spectrum of music file formats, enhancing the methodology's versatility as well as its robustness by further reducing its dependency on third-party python packages. lastly, antconc was mainly used for exemplary reasons within this study. the incorporation of advanced natural language processing (nlp) packages, such as nltk or spacy, directly into the aforementioned interactive music analysis tool (i-mat), as outlined towards the end of section 2.2, presents a significant opportunity. this integration would enable more sophisticated analysis capabilities, allowing for the calculation, exportation, and visualization of results within a unified platform. such an approach could make the analysis process more user-friendly and accessible, particularly for scholars and students who are new to the field of digital musicology. it would further lower the barriers to entry in this interdisciplinary field, enhancing the appeal and reach of digital musicological studies. we can assume that this research not only makes a step towards bridging the gap between computational linguistics and musicology but also lays the groundwork for a more integrated and holistic approach to the analysis of music and language.