Background Hum Annoyance Decibel Band Classifier

Audit independently weighted background tonal-hum measurements against caller context-specific annoyance intervals; no equal dB perceptual response assumed. Unknown criteria explicitly distinguish zero placeholders. Caller independently establishes matching units, evidence, method edition and applicability; output is not compliance, diagnosis, work-safety approval, geological identity or validated hazard prediction.

Description

Background-noise measurements need a declared weighting, measurement protocol and comparison band.

Background-noise measurements need a declared weighting, measurement protocol and comparison band.^[1]

Classifying submitted known values does not install occupational hearing criteria or identify a noise source. Use this tool to calculate background Hum Annoyance Decibel Band Classifier from the supplied measurements and assumptions.

When to use Background Hum Annoyance Decibel Band Classifier

Use this tool to calculate background Hum Annoyance Decibel Band Classifier from the supplied measurements and assumptions. Establish the observation protocol, units, task and reference population or review criterion before entering measurements.

How to use Background Hum Annoyance Decibel Band Classifier and understand its results

Establish the observation protocol, units, task and reference population or review criterion before entering measurements.
Enter observedHumFrequencyWeightedDecibels, callerVerifiedHumFrequencyWeightedDecibelsLower, callerVerifiedHumFrequencyWeightedDecibelsUpper, knownHumFrequencyWeightedDecibelsCriteriaFlags, using the field meanings and rules below, then run the tool.
Read signedKnownMargins, knownReviewIndices, unknownCriteriaIndices. Interpret the fields using their units, signs and evidence conventions below.
If input validation fails, correct the reported field and run again. Before applying the result, check reference scope, model assumptions and any missing evidence or placeholder values.

Input field	Meaning	Value type	Requirement
observedHumFrequencyWeightedDecibels	Observed hum frequency weighted decibels	list of number	Required
callerVerifiedHumFrequencyWeightedDecibelsLower	Caller verified hum frequency weighted decibels lower; caller assertion or measurement requiring independent evidence	list of number	Required
callerVerifiedHumFrequencyWeightedDecibelsUpper	Caller verified hum frequency weighted decibels upper; caller assertion or measurement requiring independent evidence	list of number	Required
knownHumFrequencyWeightedDecibelsCriteriaFlags	Known hum frequency weighted decibels criteria flags; binary 1=known, 0=unknown, in the order described below	list of number	Required

Enter nonempty aligned finite observations, lower bounds, upper bounds and binary known flags. Require lower≤upper per item. Known signed margins are min(observed−lower,upper−observed); a negative margin places that zero-based index in knownReviewIndices. Equality is inside. Unknown entries receive placeholder margin 0 and enter unknownCriteriaIndices; their numeric values and bounds are still validated. No standard threshold, measurement or general suitability assessment is installed.

margin_i=known_i ? min(value_i−lower_i,upper_i−value_i) : 0; review={known and margin<0}; unknown={known_i=0}

Use the exact units, boundary rules, ordering and missing-evidence conventions above.

Output field	Meaning	Value type
signedKnownMargins	Per-item smaller distance to either bound; negative outside, zero placeholder for unknown items	list of number
knownReviewIndices	Zero-based indices of known observations strictly outside the supplied inclusive window	list of values
unknownCriteriaIndices	Zero-based unassessed criterion indices; placeholder margins are not passes	list of number

json

{
  "input": {
    "observedHumFrequencyWeightedDecibels": [
      2,
      5
    ],
    "callerVerifiedHumFrequencyWeightedDecibelsLower": [
      1,
      1
    ],
    "callerVerifiedHumFrequencyWeightedDecibelsUpper": [
      3,
      4
    ],
    "knownHumFrequencyWeightedDecibelsCriteriaFlags": [
      1,
      0
    ]
  },
  "output": {
    "signedKnownMargins": [
      1,
      0
    ],
    "knownReviewIndices": [],
    "unknownCriteriaIndices": [
      1
    ]
  }
}

Limitations and assumptions

Classifying submitted known values does not install occupational hearing criteria or identify a noise source. Interpretation depends on the units, sampling scope and exact model described above.

All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse evidence arrays fail validation; the specific bounds and alignment requirements are stated above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful result reporting false, no matches or missing evidence.

Alternative or Complementary methods

Use Noise Dose Calculator for this related task: Noise dose accumulates exposure durations using a declared criterion level, criterion duration and exchange rate.

Use Noise Attenuation Distance Calculator when its separate inputs and assumptions fit the question: A point-source attenuation model combines geometric spreading with supplied additional losses.

References

Background Hum Annoyance Decibel Band Classifier — tool.io

Similar or alternative tools

Noise Dose Calculator
Noise dose accumulates exposure durations using a declared criterion level, criterion duration and exchange rate.
Noise Attenuation Distance Calculator
A point-source attenuation model combines geometric spreading with supplied additional losses.