Improving Auditory Memory for Analysing Contemporary Music: The Use of Computational Analytical Tools as an Aid of Listening
Music analysis involves categorising and interpreting sonic elements to uncover the structure and meaning of a work. In contemporary music studies, analysts often face methodological challenges in this process, especially when dealing with works that contain high degrees of complexity and ambiguity in terms of timbre, texture and temporal structure. This paper proposes a methodological model for analysing spatiotemporal complexities commonly observed in contemporary repertoires, utilising computational tools to enhance auditory memory and expand interpretative possibilities.
Auditory memory plays a pivotal role in aural analysis, an approach that serves as a valuable alternative or complement to traditional score-based analysis. Rooted in Pierre Schaeffer’s typomorphology of objets sonores and the work of other analysts in electroacoustic music studies, the general principles of aural analysis can be outlined in a three-step process: 1) attentive listening to the acoustic properties of sounds, 2) describing and categorising their sonic variations, and 3) assessing their functions within a large-scale formal structure. Computational sound visualisation tools are frequently employed in this process to assist in transcribing and retaining musical events that are either absent from the score or difficult to interpret aurally due to textural complexities and/or timbral elusiveness. Despite their increasing use, however, the full potential of these tools remains largely unexplored in contemporary music studies. By digitally decomposing the transformation processes of ambiguous musical flows and supporting the organisation and structuring of auditory memory, computational analysis of audio data and various visualisation methods can deepen our understanding of both local sonic morphology and large-scale formal trajectory.
In line with these considerations, the paper investigates how specialised computer interfaces can facilitate music analytical processes. Two research questions guide this investigation: 1) How can we analyse a stream of sonic textures; and 2) How can we outline the formal structure of a work that embraces extremes of sonic energy and polyrhythmic intricacy? To explore these questions, we have developed muScope, a new computer program that enables users to browse within high-resolution sonograms in tandem with a range of graphical representations capturing audio, timbral, rhythmic and structural descriptions. The analysis of spectral “fluctuations” allows for the identification of rapid pulsations at the middle ground between rhythm and timbre. Self-similarity matrix representations can serve as a tool for outliing the structural dividsion of the audio data based on various sonic attributes. We integrate these visual representations into an analytical workflow deisgned to support the construction of a composition’s formal structure. Our methods are demonstrated through an analysis of excerpts from Kaija Saariaho’s Io for large ensemble and electronics (1986–87) and Raphaël Cendo’s Corps for piano and ensemble (2015). This integrated analytical approach offers new insights into the interplay between musical perception, memory and analytical interpretation using digital tools.
Keywords: music analysis, computational analysis, contemporary music
Biographies
Marina Sudo is a musicologist and postdoctoral researcher at the University of Music and Performing Arts Graz (KUG), Institut 1. Her research focuses on twentieth- and twentieth-first-century music, with particular interest in post-tonal compositional techniques, instrumental and electroacoustic music, noise music, and issues surrounding listening, aesthetics and analysis. Her work has been published as several book chapters, MGG Online entries and journal articles, including in Music Analysis, Musicologica Austriaca and Organised Sound. After completing her PhD followed by three years of postdoctoral research at the University of Leuven, she is currently leading a project “Listening Beyond the Score: Integrated Analysis of Twentieth- and Twentieth-first-century Music”, funded by the ESPRIT Programme of the FWF Austrian Science Fund.
University of Music and Performing Arts Graz, Austria – marina.sudo@kug.ac.at
Olivier Lartillot, a researcher at RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion at the University of Oslo, focuses on computational music analysis and artificial intelligence. He obtained funding from the Research Council of Norway under the FRIPRO-IKTPLUSS program for the MIRAGE project (2020-2023), which aims to enhance computers’ ability to understand music and develop technologies to facilitate music appreciation and engagement. He currently leads muScribe, a project dedicated to creating advanced AI systems that transcribe audio recordings into detailed music scores, offering this service to music archives, cultural institutions, music publishers, and copyright organizations. Lartillot is the designer of MIRtoolbox, a recognized tool for music feature extraction from audio. He also specializes in symbolic music analysis, notably in sequential pattern mining. As part of his five-year Academy of Finland research fellowship, he developed MiningSuite, an analytical framework that integrates audio and symbolic research for comprehensive music analysis.
University of Oslo – olivier.lartillot@imv.uio.no