Outdoor Concert Noise: The Kitchener, Ontario Experience
Bibliographic record
Abstract
IntroductionOutdoor music festivals are socially, culturally and economically important to the communities that host them.However, sound generated by these events, have the potential to disturb surrounding noise-sensitive environs, especially area residents.Sensitivities depend on many factors, including but not limited to duration, audibility of the event, type of music, and even non-acoustic associations of the concert and duration.Truly, one person's music can be another person's noise.The challenge facing municipalities and promotors holding these events, is to provide sufficiently high-level sound reinforcement to meet patron audio quality expectations while keeping off-site sound levels sufficiently low, to avoid disturbing surrounding communities.Since 2015, an Electronic Dance Music (EDM) event, the EverAfter Music Festival has been held in Kitchener, Ontario in early June.Inaugurally, a two-day (Friday -Saturday) event, the festival was extended to three-days (Friday-Sunday) in 2016 and 2017.During, the three-year history of the event, noise complaints to the City of Kitchener Noise-By-law Office have varied significantly, with little or no complaints in 2016, while 57 complaints were received in 2015 and about twice as many in 2017.The complaint history, and key characteristics of the EverAfter event are summarized below: Total (complaints) -2015: (57), 2016: (7), 2017: (130).Concert duration (days) -2015 (2), 2016 & 2017 (3).Multi-stage concert -all years.Main stage orientation -2015 (south), other years (east).Performances -1100 h to 2300 h (headliners -2030 h).High SPL / low frequency program -120 dBA @ 140 ft.Some surrounding locales in semi urban areas bordering farm and ravine lands.The City granted a Noise-by-law exemption, with festival sound levels limited to 65 dBA at any residences.Given the complaint record, can the variability be explained and are there approaches available to help provide greater event compatibility with residents' concerns?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".