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Associations Between Musicology and Music Information Retrieval 12th International Society for Music Information Retrieval Conference (ISMIR 2011) ASSOCIATIONS BETWEEN MUSICOLOGY AND MUSIC INFORMATION RETRIEVAL Kerstin Neubarth Mathieu Bergeron Darrell Conklin Canterbury Christ Church University CIRMMT Universidad del Pa´ıs Vasco Canterbury, UK McGill University San Sebastian,´ Spain [email protected] Montreal, Canada and IKERBASQUE, Basque Foundation for Science ABSTRACT formed a citation analysis of papers within ISMIR, counting references to individual authors and papers. It briefly ad- A higher level of interdisciplinary collaboration between dressed citer motivations such as identification of data and music information retrieval (MIR) and musicology has been methods or paying homage, and concluded that: “Without a proposed both in terms of MIR tools for musicology, and more in-depth analysis of the individual contexts surround- musicological motivation and interpretation of MIR research. ing each citation, it is difficult to tease out the precise mo- Applying association mining and content citation analysis tivations for all the references” (p. 61). Functions of refer- methods to musicology references in ISMIR papers, this pa- ences to one particular study were discussed in the editorial per explores which musicological subject areas are of inter- to the JNMR Special Issue on MIR in 2008 [1]. est to MIR, whether references to specific musicology areas This study extends previous work in several ways: It are significantly over-represented in specific MIR areas, and analyses inter-disciplinary references (musicology cited in precisely why musicology is cited in MIR. MIR) rather than intra-MIR references; also, the references are analysed at the level of MIR and musicology subject cat- 1. INTRODUCTION egories instead of individual papers. The quantitative analy- At the tenth anniversary ISMIR 2009 several contributions sis goes beyond citation counts; association mining is used discussed challenges in the further development of music here to yield interdisciplinary associations. In addition, ci- information retrieval (MIR) as a discipline, including re- tation contexts are analysed in depth to reveal functions of quests for deeper musical motivation and interpretation of musicology citations in ISMIR papers, taking into account MIR questions and results, and envisaging closer interaction both MIR-specific functions and more general referencing with source disciplines such as computer science, cognitive purposes to allow comparison with existing studies. science and musicology [4, 9, 20]. Suggestions for interdisciplinary collaboration have of- 2. DATA SELECTION AND ANALYSIS ten considered musicology as a target discipline, emphasis- ing the usefulness of MIR tools to musicology (e.g. [18]). This section presents the corpus development and the as- Occasionally mutual benefits have been explored (e.g. [15]). sociation mining and citation analysis methods used for ex- The current study addresses associations between MIR and tracting and analysing associations between musicology and musicology as a source discipline. It presents a systematic music information retrieval. analysis of how MIR, as represented at ISMIR, has drawn on musicology so far, by applying data mining and content 2.1 Sampling citation analysis to musicology references in ISMIR publi- From the cumulative ISMIR proceedings (www.ismir.net) cations. as a sampling frame, first all available full papers from 2000 Related quantitative ISMIR surveys mainly analyse top- until 2007, and all oral/plenary session papers for 2008 to ics and trends [3, 7, 10]. The study by Lee et al. [10] per- 2010, were selected. This resulted in 416 papers. Then the reference lists of those papers were screened and a purposive Permission to make digital or hard copies of all or part of this work for sample was taken of all papers which contain references to personal or classroom use is granted without fee provided that copies are musicology as a source discipline, excluding self-citations not made or distributed for profit or commercial advantage and that copies and references to other ISMIR papers. The final analysis bear this notice and the full citation on the first page. corpus consisted of 184 papers. These papers are identified c 2011 International Society for Music Information Retrieval. by their IDs within the cumulative proceedings (e.g. ID135). 429 Poster Session 3 2.2 Encoding History, criticism & philosophy History of music (8) The 184 papers of the analysis corpus were labelled accord- Philosophy of music & music semiotics (3) ing to their MIR research topic and the musicology areas Textual criticism, archival research & bibliography (22) that they cite, using the following categorisations. Electronic & computer music (7) MIR Categories. In a first step of encoding, the 184 Popular & jazz music studies (5) papers were classified into MIR research areas (Table 1). Film music studies (1) As no single standardised and comprehensive taxonomy of Theory & analysis MIR topics exists [3,6], an organisation of topics was devel- Music theory & analysis (36) oped based on ISMIR calls and programs, harmonising cate- Performance studies (6) gories across conferences. Each ISMIR paper is assigned to Ethnomusicology exactly one MIR category. Numbers in brackets in Table 1 Ethnomusicology (non-Western) (5) indicate the number of papers in each category. Ethnomusicology (folk music) (9) Ethnomusicology (other) (1) Systematic Musicology Research paradigms (6) Acoustics (11) Epistemology, interdisciplinarity Psychology of music (perception & cognition) (93) Representation & metrics (24) Psychology of music (emotion & affect) (8) Representation, metrics, similarity Psychology of music (other) (1) Data & metadata (11) Sociology & sociopsychology (18) Databases, data collection & organisation, metadata, anno- tation Transcription (42) Table 2. Thematic categories of musicology. Segmentation, voice & source separation, alignment, beat tracking & tempo estimation, key estimation, pitch tracking & spelling Musicology Categories. In a second step, the musicol- OMR (5) ogy references in the 184 papers were assigned to musicol- Optical music recognition, optical lyrics extraction ogy areas (Table 2). As the interest of this study is in mu- Computational music analysis (21) sicology as a source discipline, the labels used are based Pattern discovery & extraction, summarisation, chord la- on traditional subject organisations (e.g. [11, 14, 16]) rather belling, musical analysis (melody & bassline, harmonic, than more recent developments such as empirical or com- rhythm and form analysis) putational musicology which potentially overlap with music Retrieval (19) information retrieval (e.g. [18]). Category counts in Table 2 Query-by-example refer to the number of ISMIR papers citing this musicology Classification (32) area one or more times. A paper may reference more than Genre classification, geographical classification, artist clas- one musicology category. sification & performer identification, instrument-voice clas- sification (instrument recognition, instrument vs voice dis- 2.3 Association Mining tinction, classification of vocal textures), mood & emotion classification Data mining of the analysis corpus is used to reveal asso- Recommendation (5) ciations between papers in specific MIR categories and pa- Recommendation methods & systems, playlist generation, pers that have citations to specific musicology categories. recommendation contexts For every musicology category A and MIR category B, the Music generation (4) support (number of papers) s(A) and s(B) were computed. Music prediction, improvisation, interactive instruments Also the support s(A, B) of an association hA, Bi (number A Software systems (10) of papers containing references to musicology category B Prototypes & toolboxes, user interfaces & usability, visuali- which are also in MIR category ) and the statistical signif- sation icance of the association were computed. User studies (5) The null hypothesis is that for an association hA, Bi the User behaviour (music discovery, collection organisation) two categories are statistically independent, i.e. that the pro- portion of papers citing musicology category A that are in MIR category B does not differ significantly from the rel- Table 1. Thematic categories and examples of topics of ative frequency of MIR category B in the general popula- MIR research. tion. Given the small corpus and low counts for many cat- 430 12th International Society for Music Information Retrieval Conference (ISMIR 2011) egories, the appropriate test for statistical independence is perception and cognition research and acoustics are cited by Fisher’s one-tailed exact test on a 2x2 contingency table [5]. different subsets of papers on transcription. For an association hA, Bi with support s(A, B), this gives Comparing Figure 1 against MIR topics in Table 1, addi- the probability (p-value) of finding s(A, B) or more papers tional links could have been expected e.g. between papers of category B in s(A) samples (without replacement) in on data and metadata and references to textual criticism, n = 184 total papers. If the computed p-value is less than archival research and bibliography or history of music [15] the significance level α = 0.05, then we reject the null hy- or between classification and performance studies (for per- pothesis that the categories are independent. former identification), acoustics (for instrument-voice clas- Prior to computing the p-values, the counts of all MIR
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