Computational and Cognitive Musicology Projects

Music Perception and Cognition

Sclae-degree Qualia Ratings across changing harmony

How is it that purely instrumental music can make us feel sad, or give us goosebumps? Why does a song get “stuck in your head” but a lecture doesn’t? What musical features cause confusion about the location of the downbeat (or where to tap your foot)? These are the kinds of questions we aim to answer in the field of music perception and cognition. Our lab conducts controlled behavioral experiments to try to untangle these complex problems.

Publications

Theory vs Practice

Theory versus practice

As musicians and scientists, we seek to provide empirical support (or refutation) for claims and common assumptions concerning the structural organization of music. These questions are typically carried out by performing statistical modeling on a corpus (or collection) of musical data. Some recent questions have included: Did Renaissance composers write counterpoint in a way that is consistent with how their contemporaries taught it? Are popular music melodies harmonized in a different way than classical melodies? How are note-transition probabilities affected by the particular harmony that underlies them?

Publications

  • Arthur, C., Schubert, P., & Cumming, J. (2018). "The Role of Structural Tones in Establishing Mode in Renaissance Counterpoint," in Proceedings of the 15th International Conference for Music Perception and Cognition (Montreal, Canada).
  • Arthur, C. (2017). "Taking Harmony into Account: The Effect of Harmony on Melodic Probability," Music Perception, 34(4): 405–423
  • Arthur, C. (2016). "A Corpus Approach to the Classification of Non-chord Tones Across Genres," in Proceedings of the 14th International Conference for Music Perception and Cognition (San Francisco, CA, USA): 74–76

Symbolic Corpora Building

Symbolic Corpora Building programs

A fast-growing area of music scholarship is computational musicology. Music historians and theorists need access to digital representations of musical scores and performances in order to facilitate empirical approaches to their research. Unfortunately, at present there is a severe lack of symbolic musical data, and existing data is biased toward Baroque and Classical music, and largely consists of vocal and piano repertoire. In addition, because there is no single standard for the representation of symbolic data, existing datasets appear in many different formats, and with varying levels of accuracy and completeness. Work in this area seeks to address these issues, while also providing tools for the analysis of the symbolic data.

Publications

  • Condit-Schultz, N. & Ju, Y. & Fujinaga, I. (2018). "A Flexible Approach to Automated Harmonic Analysis: Multiple Annotations of Chorales by Bach and Prætorius," in Proceedings of the International Society of Music Information Retrieval (Paris, France).
  • Léveillé Gauvin, H., Condit-Schultz, N., Arthur, C. (2017). "Supplementing Melody, Lyrics, and Acoustic Information to the McGill Billboard Database," in DH2017: premiere annual conference of the international Alliance of Digital Humanities Organizations (Montreal, Canada).
  • Condit-Schultz, N. (2016). “The Musical Corpus of Flow: A Digital Corpus of Rap Transcriptions,” Empirical Musicology Review, 11(2): 124–146.
  • Devaney, J., Arthur, C., Condit-Schultz, N., & Nisula, K. (2015). “Theme And Variation Encodings with Roman Numerals (TAVERN): a New Data Set for Symbolic Music Analysis,” in Proceedings of the International Society of Music Information Retrieval (Málaga, Spain): 728–734.

Computational Musicology Research

Computer musicology tools and systems

Music theorists, musicologists, performers, composers, and conductors are frequently faced with real-world challenges that can typically only be solved through manual labor. Such problems include: “by ear” transcription of a melody embedded in a multi-part performance; Roman numeral and functional harmonic analysis; segmentation and labeling of a piece into distinct tonal (or key) areas; labeling and identification of cadences, structural boundaries, or “repeated” musical fragments; annotation of lyrics, chord symbols, and more. We apply heuristic and machine learning approaches to understand or extract these higher-level musical features from symbolic musical data, with the aim of automating or facilitating these real-world tasks.

Publications

  • Condit-Schultz, N. & Ju, Y. & Fujinaga, I. (2018). "A Flexible Approach to Automated Harmonic Analysis: Multiple Annotations of Chorales by Bach and Prætorius," in Proceedings of the International Society of Music Information Retrieval (Paris, France).
  • Garfinkle, D., Arthur, C., Schubert, P., Cumming, J., & Fujinaga, I. (2017). "PatternFinder: Content-based Music Retrieval with music21," in Proceedings of the 4th International Digital Libraries for Musicology workshop (Shanghai, China): 5–8.
  • Ju, Y., Condit-Schultz, N., Arthur, C., & Fujinaga, I. (2017). “Non-chord Tone Identification Using Deep Neural Networks,” in Proceedings of the 4th International Digital Libraries for Musicology workshop (Shanghai, China): 13–16.
  • Condit-Schultz, N. (2016). “The Musical Corpus of Flow: A Digital Corpus of Rap Transcriptions,” Empirical Musicology Review, 11(2): 124–146.
  • Arthur, C. (2016). “A Corpus Approach to the Classification of Non-chord Tones Across Genres,” in Proceedings of the 14th International Conference for Music Perception and Cognition (San Francisco, USA): 74–76.