What is it about?

Our goal is to improve methods of quantifying tonal noise from heating, ventilation, and air-conditioning systems and then to use those methods toward improving the prediction of annoyance caused by such noise. This paper present results from two studies: one using multidimensional scaling (MDS) analysis to determine important perceptual characteristics from tonal noise signals, and the other using perceptual weight analysis (PWA) to understand the impact of multiple tones in a signal (which is common).

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Why is it important?

Assorted indoor noise criteria are used to qualify acceptable noise levels in indoor environments, but they do not always account well for noise with tonal components, as are commonly generated from heating, ventilation, and air-conditioning systems. Our work helps towards tying objective metrics reliably to subjective perception, particularly when the noise includes troublesome tones.


Results from the MDS study show that subjects perceived differences in tonal noise signals related to the signal's tonality, loudness, sharpness, and roughness. Including metrics for these perceptions (except roughness) improves the performance of earlier annoyance prediction models. From the PWA study, we were able to produce a perceptual weighting function that can be applied to calculate a proposed weighted-sum tonal audibility metric. Utilizing the proposed metric instead of the traditional tonal audibility metric improves annoyance prediction.

Dr. Lily M Wang
University of Nebraska System

Read the Original

This page is a summary of: Investigating multidimensional characteristics of noise signals with tones from building mechanical systems and their effects on annoyance, The Journal of the Acoustical Society of America, January 2020, Acoustical Society of America (ASA),
DOI: 10.1121/10.0000487.
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