jMIR (as a whole)

Vatolkin, I., and C. McKay. 2022. Multi-objective investigation of six feature source types for multi-modal music classification. Transactions of the International Society for Music Information Retrieval 5 (1): 1–19.

McKay, C. 2010. Automatic music classification with jMIR. Ph.D. Thesis. McGill University, Canada.

McKay, C., J. A. Burgoyne, J. Hockman, J. B. L. Smith, G. Vigliensoni, and I. Fujinaga. 2010. Evaluating the genre classification performance of lyrical features relative to audio, symbolic and cultural features. Proceedings of the International Society for Music Information Retrieval Conference. 213–8.

McKay, C., and I. Fujinaga. 2010. Improving automatic music classification performance by extracting features from different types of data. Proceedings of the ACM SIGMM International Conference on Multimedia Information Retrieval. 257–66.

McKay, C., J. A. Burgoyne, and I. Fujinaga. 2009. jMIR and ACE XML: Tools for performing and sharing research in automatic music classification. Presented at the ACM/IEEE Joint Conference on Digital Libraries Workshop on Integrating Digital Library Content with Computational Tools and Services.

McKay, C., and I. Fujinaga. 2009. jMIR: Tools for automatic music classification. Proceedings of the International Computer Music Conference. 65–8.

McKay, C., and I. Fujinaga. 2008. Combining features extracted from audio, symbolic and cultural sources. Proceedings of the International Conference on Music Information Retrieval. 597–602.

jSymbolic

McKay, C., and M. E. Cuenca. 2024. Harmonious research collaborations in computational musicology. Presented at the Digital Technologies Applied to Music Research Conference.

McKay, C., and J. Cumming. 2024. New tools for old questions: Applying feature extraction and machine learning to Rodin’s "The Josquin Canon at 500". Presented at the Medieval and Renaissance Music Conference.

McKay, C., and J. Cumming. 2024. Using feature-based composer classification to test musicological evidence for Josquin attribution. Extended Abstracts for the Late-Breaking Demo Session of the 25th International Society for Music Information Retrieval Conference.

Cuenca, M. E., and C. McKay. 2023. The stylistic origin of the anonymous 16th century masses transcribed by Siro Cisilino (1903-1987) at the Fondazione Cini: A statistical and machine learning approach. Presented at the Medieval and Renaissance Music Conference.

McKay, C. 2023. Feature extraction, feature-indexed databases, features in musicology and evolution with feature; Also, features. Presented at the CIRMMT Scientific Event, McGill University, Montreal, Canada. 25 May 2023.

McKay, C. 2023. From jSymbolic 2 to 3: More musical features. Proceedings of the International Symposium on Computer Music Multidisciplinary Research. 752–755.

McKay, C., J. Cumming, and I. Fujinaga. 2023. Rhythmic, melodic and vertical n-gram features as a means of studying symbolic music computationally. Presented at the Digital Humanities Conference.

McKay, C., and R. Mizrahi. 2023. SIMSSA DB: Go Jump in the (Data) Lake. Presented at the LinkedMusic Workshop, McGill University, Montreal, Canada. 21 October 2023.

Cuenca, M. E., and C. McKay. 2022. Musical influences on the masses of Pedro Fernández Buch (c. 1574- 1648): A stylistic comparison using statistical analysis. Presented at the Medieval and Renaissance Music Conference.

McKay, C., and M. E. Cuenca. 2022. Influencias musicales en las misas y motetes de Cristóbal de Morales y Francisco Guerrero: Una aproximación estadística. In Musicología en transición, eds. J. Marín-López, A. Mazuela-Anguita and J. J. Pastor-Comín, 1031–1052. Madrid, Spain: Sociedad Española de Musicología.

McKay, C. 2022. SIMSSA DB: An introduction. Presented at the CIRMMT LinkedMusic Workshop on Music Databases, McGill University, Montreal, Canada. 18 November 2022.

McKay, C. 2022. SIMSSA DB: Some details. Presented at the LinkedMusic Prjoect Meeting, McGill University, Montreal, Canada. 19 November 2022.

McKay, C., and J. Cumming. 2022. Summary features as the basis for content-based queries of symbolic music repositories. Presented at the Congress of the International Association of Music Libraries, Archives and Documentation Centres.

Vatolkin, I., and C. McKay. 2022. Stability of symbolic feature group importance in the context of multi-modal music classification. Proceedings of the International Society for Music Information Retrieval Conference. 469–76.

Cuenca, M. E., and C. McKay. 2021. Exploring musical style in the anonymous and doubtfully attributed mass movements of the Coimbra manuscripts: A statistical and machine learning approach. Journal of New Music Research 50 (3): 199–219.

Cuenca, M. E., and C. McKay. 2021. Influencias musicales en las misas y motetes de Cristóbal de Morales y Francisco Guerrero: Una aproximación estadística. Presented at the Congreso de la Sociedad Española de Musicología.

Cumming, J., and C. McKay. 2021. Using corpus studies to find the origins of the madrigal. Proceedings of the Future Directions of Music Cognition International Conference. 38–42.

McKay, C. 2021. Exploring composer attribution in motet cycles using machine learning. Gaffurius Codices Online, Schola Cantorum Basiliensis.

McKay, C., and M. E. Cuenca. 2021. Musical influences on the masses and motets of Cristóbal de Morales and Francisco Guerrero: A statistical approach. Presented at the Medieval and Renaissance Music Conference.

Rodriguez-Garcia, E., and C. McKay. 2021. Ave festiva ferculis: Exploring attribution by combining manual and computational analysis. Presented at the Medieval and Renaissance Music Conference.

Rodríguez-García, E., and C. McKay. 2021. Composer attribution of Renaissance motets: A case study using statistical features and machine learning. In The Anatomy of Iberian Polyphony Around 1500, eds. E. Rodríguez-García and J. P. d’Alvarenga, 401–38. Kassel, Germany: Edition Reichenberger.

McKay, C., R. Adamian, J. Cumming, and I. Fujinaga. 2020. Exploring Renaissance music using n-gram aggregates to summarize local musical content. Presented at the Medieval and Renaissance Music Conference.

McKay, C., J. Cumming, and I. Fujinaga. 2020. Lessons learned in a large-scale project to digitize and computationally analyze musical scores. Digital Scholarship in the Humanities.

Cuenca, M. E., and C. McKay. 2019. Análisis estadístico de misas ibéricas renacentistas a través del software jSymbolic. Presented at the El análisis musical actual: Marco teórico e interdisciplinariedad conference.

Cuenca, M. E., and C. McKay. 2019. Exploring musical style in the anonymous and doubtfully attributed mass movements of the Coimbra manuscripts: A statistical approach. Presented at the Medieval and Renaissance Music Conference.

Cumming, J., C. McKay, N. Nápoles López, and S. Margot. 2019. Contrapuntal style: Pierre de la Rue vs. Josquin Des Prez. Presented at the CIRMMT Workshop on SIMSSA (Single Interface for Music Score Searching and Analysis), McGill University, Montreal, Canada. 21 Saturday 2019.

Hopkins, E, Y. Ju, G. Polins Pedro, C. McKay, J. Cumming, and I. Fujinaga. 2019. SIMSSA DB: Symbolic music discovery and search. Poster presentation at the International Conference on Digital Libraries for Musicology.

Hopkins, E., G. Polins Pedro, Y. Ju, C. McKay, J. Cumming, and I. Fujinaga. 2019. SIMSSA DB: A brief overview of the data model. Presented at the DACT (Digital Analysis of Chant Transmission) Workshop, McGill University, Montreal, Canada. 21 Saturday 2019.

Ju, Y., G. Polins Pedro, C. McKay, E. Hopkins, J. Cumming, and I. Fujinaga. 2019. Enabling music search and analysis: A database for symbolic music files. Presented at the Music Encoding Conference.

McKay, C., and M. E Cuenca. 2019. CRIM, machine learning and big data: A case study on the Coimbra manuscripts. Presented at the Counterpoints: Renaissance Music and Scholarly Debate in the Digital Domain conference.

McKay, C., E. Hopkins, G. Polins Pedro, Y. Ju, A. Kam, J. Cumming, and I. Fujinaga. 2019. A collaborative symbolic music database for computational research on music. Presented at the Medieval and Renaissance Music Conference.

McKay, C., J. Cumming, and I. Fujinaga. 2019. Lessons learned in a large-scale project to digitize and computationally analyze musical scores. Presented at the Digital Humanities Conference.

McKay, C., and R. Adamian. 2019. jSymbolic in 2019: Updates and improvements. Presented at the CIRMMT Workshop on SIMSSA (Single Interface for Music Score Searching and Analysis), McGill University, Montreal, Canada. 21 Saturday 2019.

McKay, C. 2019. SIMSSA DB: A collaborative musicological research database. Presented at the Digital Humanities Conference Digital Musicology Study Group.

Cumming, J., and C. McKay. 2018. Contrapuntal style: Josquin Desprez vs. Pierre de la Rue. Presented at the Conference on Pierre de la Rue and Music at the Habsburg-Burgundian Court.

Cumming, J., C. McKay, J. Stuchbery, and I. Fujinaga. 2018. Methodologies for creating symbolic corpora of Western music before 1600. Proceedings of the International Society for Music Information Retrieval Conference. 491–8.

Cumming, J., and C. McKay. 2018. Revisiting the origins of the Italian madrigal using machine learning. Presented at the Medieval and Renaissance Music Conference.

McKay, C., J. Cumming, and I. Fujinaga. 2018. jSymbolic 2.2: Extracting features from symbolic music for use in musicological and MIR research. Proceedings of the International Society for Music Information Retrieval Conference. 348–54.

McKay, C. 2018. jSymbolic: A software application for music information retrieval and analysis. Invited Speaker. CESEM, Nova University of Lisbon, Lisbon, Portugal. 8 March 2018.

McKay, C. 2018. Performing statistical musicological research using jSymbolic and machine learning. Presented at The Anatomy of Polyphonic Music around 1500 International Conference.

McKay, C. 2018. SIMSSA DB: A database for computational musicological research. Presented at the International Association of Music Libraries, Archives and Documentation Centres International Congress SIMSSA Workshop.

McKay, C., J. Cumming, and I. Fujinaga. 2017. Characterizing composers using jSymbolic2 features. Extended Abstracts for the Late-Breaking Demo Session of the 18th International Society for Music Information Retrieval Conference.

McKay, C., T. Tenaglia, J. Cumming, and I. Fujinaga. 2017. Using statistical feature extraction to distinguish the styles of different composers. Presented at the Medieval and Renaissance Music Conference.

McKay, C., T. Tenaglia, and I. Fujinaga. 2016. jSymbolic2: Extracting features from symbolic music representations. Extended Abstracts for the Late-Breaking Demo Session of the 17th International Society for Music Information Retrieval Conference.

McKay, C. 2012. Classifying music with jMIR. Invited Speaker. Department of Languages and Science of Computation, University of Malaga, Spain. 10 January 2012.

McKay, C., and I. Fujinaga. 2007. Style-independent computer-assisted exploratory analysis of large music collections. Journal of Interdisciplinary Music Studies 1 (1): 63–85.

McKay, C., and I. Fujinaga. 2006. jSymbolic: A feature extractor for MIDI files. Proceedings of the International Computer Music Conference. 302–5.

jProductionCritic

McKay, C. 2013. jProductionCritic: An educational tool for detecting technical errors in audio mixes. Proceedings of the International Society for Music Information Retrieval Conference. 71–6.

jSongMiner

McKay, C., and D. Bainbridge. 2011. A musical web mining and audio feature extraction extension to the Greenstone digital library software. Proceedings of the International Society for Music Information Retrieval Conference. 459–464.

ACE and ACE XML

McKay, C., and I. Fujinaga. 2013. Expressing musical features, class labels, ontologies, and metadata using ACE XML 2.0. In Structuring Music Through Markup Language: Designs and Architectures, ed. J. Steyn, 48–79. Hershey, PA: IGI Global.

McKay, C., J. A. Burgoyne, J. Thompson, and I. Fujinaga. 2009. Using ACE XML 2.0 to store and share feature, instance and class data for musical classification. Proceedings of the International Society for Music Information Retrieval Conference. 303–8.

Thompson, J., C. McKay, J. A. Burgoyne, and I. Fujinaga. 2009. Additions and improvements to the ACE 2.0 music classifier. Proceedings of the International Society for Music Information Retrieval Conference. 435–40.

McKay, C., and I. Fujinaga. 2007. Style-independent computer-assisted exploratory analysis of large music collections. Journal of Interdisciplinary Music Studies 1 (1): 63–85.

McKay, C., R. Fiebrink, D. McEnnis, B. Li, and I. Fujinaga. 2005. ACE: A framework for optimizing music classification. Proceedings of the International Conference on Music Information Retrieval. 42–9.

McKay, C., D. McEnnis, R. Fiebrink, and I. Fujinaga. 2005. ACE: A general-purpose classification ensemble optimization framework. Proceedings of the International Computer Music Conference. 161–4.

Sinyor, E., C. McKay, R. Fiebrink, D. McEnnis, and I. Fujinaga. 2005. Beatbox classification using ACE. Proceedings of the International Conference on Music Information Retrieval. 672–5.

jLyrics

McKay, C., J. A. Burgoyne, J. Hockman, J. B. L. Smith, G. Vigliensoni, and I. Fujinaga. 2010. Evaluating the genre classification performance of lyrical features relative to audio, symbolic and cultural features. Proceedings of the International Society for Music Information Retrieval Conference. 213–8.

jAudio

McEnnis, D., C. McKay, and I. Fujinaga. 2006. jAudio: Additions and improvements. Proceedings of the International Conference on Music Information Retrieval. 385–6.

McEnnis, D., C. McKay, and I. Fujinaga. 2006. Overview of OMEN. Proceedings of the International Conference on Music Information Retrieval. 7–12.

McEnnis, D., C. McKay, I. Fujinaga, and P. Depalle. 2005. jAudio: A feature extraction library. Proceedings of the International Conference on Music Information Retrieval. 600–3.

jWebMiner

Vigliensoni, G., C. McKay, and I. Fujinaga. 2010. Using jWebMiner 2.0 to improve music classification performance by combining different types of features mined from the web. Proceedings of the International Society for Music Information Retrieval Conference.

McKay, C., and I. Fujinaga. 2007. jWebMiner: A web-based feature extractor. Proceedings of the International Conference on Music Information Retrieval. 113–4.

jMusicMetaManager and Codaich

Angeles, B., C. McKay, and I. Fujinaga. 2010. Discovering metadata inconsistencies. Proceedings of the International Society for Music Information Retrieval Conference. 195–200.

McEnnis, D., C. McKay, and I. Fujinaga. 2006. Overview of OMEN. Proceedings of the International Conference on Music Information Retrieval. 7–12.

McKay, C., D. McEnnis and I. Fujinaga. 2006. A large publicly accessible prototype audio database for music research. Proceedings of the International Conference on Music Information Retrieval. 160–3.

Bodhidharma

McKay, C., and I. Fujinaga. 2005. Automatic music classification and the importance of instrument identification. Proceedings of the Conference on Interdisciplinary Musicology. CD-ROM.

McKay, C., and I. Fujinaga. 2005. The Bodhidharma system and the results of the MIREX 2005 symbolic genre classification contest. Presented at the International Conference on Music Information Retrieval.

McKay, C. 2004. Automatic genre classification as a study of the viability of high-level features for music classification. Proceedings of the International Computer Music Conference. 367–70.

McKay, C. 2004. Automatic genre classification of MIDI recordings. M.A. Thesis. McGill University, Canada.

McKay, C. and I. Fujinaga. 2004. Automatic genre classification using large high-level musical feature sets. Proceedings of the International Conference on Music Information Retrieval. 525–30.

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