Machine Listening and New Musicology: Genre Detection, Bias, and Canon Formation

Kakungulu Samuel J.

Faculty of Education, Kampala International University, Uganda

                                                                  ABSTRACT
Machine listening and new musicology intersect in the contemporary study of genre detection, algorithmic bias, and canon formation, reshaping how music is classified, interpreted, and culturally valued. Machine listening employs computational techniques to extract musical information from audio recordings, while new musicology critically interrogates the sociohistorical, political, and institutional assumptions embedded within musical discourse. Together, these approaches illuminate how automated genre classification systems influence the organization and circulation of music in digital environments. This study examines the conceptual foundations of genre, the computational methods used in genre detection, and the evaluation metrics and datasets that underpin machine-listening systems. It further analyzes how sampling bias, algorithmic opacity, and institutional preferences reinforce unequal representations of musical traditions, particularly privileging Western popular and art-music canons over non-Western and marginalized genres. The study also explores how corporations, streaming platforms, and academic institutions shape contemporary canon formation through recommendation systems, metadata infrastructures, and large-scale digital archives. Through case studies involving commercial and academic datasets, the discussion demonstrates that computational systems are  neither neutral nor purely objective, but are deeply influenced by historical, economic, and cultural assumptions. The paper argues that future computational musicology must prioritize transparency, reproducibility, inclusivity, and interdisciplinary collaboration in order to support more equitable representations of global musical cultures. Ultimately, machine listening and new musicology reveals both the possibilities and limitations of algorithmic approaches to music, highlighting the need for critical frameworks that balance technological innovation with cultural sensitivity and
ethical responsibility.
Keywords: Machine Listening, Computational Musicology, Genre Detection, Algorithmic Bias and Canon Formation.

CITE AS: Kakungulu Samuel J. (2026). Machine Listening and New Musicology: Genre Detection, Bias, and Canon Formation. INOSR HUMANITIES AND SOCIAL SCIENCES 12(1): 7-15.
https://doi.org/10.59298/INOSRHSS/2026/121.715