Manifest: A Music Composition for Soundscape and Amplified Clarinet Engaging the Windsor Hum
Bibliographic record
Abstract
Manifest is a five-movement composition for amplified clarinet and fixed multi-channel audio. The context for this work is the subject of noise pollution and the effect it can have on people and the ecosystem. In addition to the industrial noise of a manufacturing region, areas of Windsor, Ontario, are afflicted by a bothersome and persistent low frequency excitation known as the Windsor Hum. Unlike visible pollution, noise is an often-neglected concern, and much more discussion is needed to bring awareness to the health issues that arise from prolonged exposure to it. A community of resilient citizens has been enduring the disruptive consequences of industrial noise in Windsor and strive for a peaceful, healthy quality of life. Manifest is a social commentary delivered in the form of musique documentaire, a documentary-inspired approach to electroacoustic music. The foundation for the work was a series of investigative field recording trips to the Windsor region to capture the soundworld that would be used to establish the piece’s setting, while also acquiring an understanding of the predicament people were reporting. Through these recordings, additional research, and interviews with local residents, the relationship between heavy industry, industrial noise, the geography of the area, and the experiences of the people were developed into a narrative structure that was then used as the framework for the piece. The composition presents an immersive journey through Windsor, and the phenomenon of the Hum unfolds en route. The heterogeneous soundscape evokes parklands, a steel mill, and a large active river, all unified by recordings of spoken word, the clarinet, and brief transitional segments. The clarinet with live electronics represents the people and technology and serves as a common element linking these diverse sonic environments, featuring the versatility of this expressive instrument. As a compositional point of departure designed to strengthen the relationship between the clarinet and the soundscape, pitch material was derived from transcriptions and spectral analysis of the field recordings. This report presents the context, conceptualization, creative process, and analysis of this composition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.011 |
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".