Bibliographic record
Abstract
Sound mass, a musical aesthetic predicated on the grouping of many sound sources or events into a single auditory percept, has been an important feature of late-20th- and early-21st-century music. The compositional practices and aims of composers associated with sound mass have been well-documented, but sound mass has been relatively little studied from the listener's (esthesic) point of view. This dissertation begins to address questions surrounding perceptual and semantic dimensions of sound mass through a combination of theoretical, empirical, and compositional approaches. Chapter 1, "Perceptual Dimensions of Sound Mass," considers the problems of defining "sound mass," reviews extant definitions, and proposes a new one. It proceeds to review some common features of sound mass in light of perceptual principles by which they promote integration or fusion, with reference to many examples from the sound mass repertoire. A summary list of attributes to be considered in sound mass analysis is provided. Chapter 2, "Empirical Research on Sound Mass Perception," reports the findings of experiments conducted at the Music Perception and Cognition Lab at McGill university under the supervision of Dr. Stephen McAdams. The first experiment evaluates listeners' dynamic perceptions of sound mass in Ligeti's Continuum, as measured with continuous response data. The second experiment isolates excerpts from Continuum and modifies selected parameters such as register, instrumental timbre, and attack rate (tempo), in order to evaluate the extent to which these parameters influence sound mass perception. The third experiment isolates harmonic structures from Continuum to evaluate the relation between pitch density and sound mass perception when the rhythmic context is neutralized. A supplementary pilot study evaluates listeners' ratings of complex harmonies (including many of the same ones from Continuum used in experiment 3) along three categories: Bright-Dark, Pitched-Noisy, and Density.Chapter 3, "Semantic Dimensions of Sound Mass," addresses some of the general problems of musical meaning. Drawing on multidisciplinary research in music perception and cognition, semiotics, cognitive semiotics, metaphor theory, and embodied cognition, this chapter offers a dynamical model of extramusical meaning based on homology between selected musical attributes and corresponding attributes of extramusical domains. It concludes with a compilation of many metaphorical associations of sound mass, drawn from the discourse of composers and theorists of this music.Chapter 4, "Empirical Research on Sound Mass Semantics," reports on further experiments conducted at McGill's Music Perception and Cognition Lab under Dr. McAdams. In experiment 4, participants heard 40 musical excerpts featuring sound mass and related fusion-based aesthetics. They rated each excerpt on three batteries of semantic scales, drawn from the metaphorical associations of composers and theorists detailed in chapter 3. In experiment 5, participants performed the same task but with the grammatical forms of the ratings categories reversed. Chapter 5, "Compositional Application of Sound Mass: biome (2017)," describes my composition for solo trombone and wind orchestra.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".