Annoyance response to low frequency noise with tonal components: A case study on transformer noise

In order to study how acoustical factors influence annoyance responses to low frequency noise with tonal components, this research has selected 220 kV and 500 kV transformer noises as examples and an 11-point numerical scale as an evaluation tool, in which the percentage of highly annoyed (%HA) and mean annoyance (MA) are chosen to represent annoyance caused by noises. Results show that a logistic curve is well suited for describing the exposure–response relationship between the A-weighted equivalent sound pressure level (LAeq) and %HA (or MA) of the transformer noise. With the same LAeq, 220 kV transformer noise is more annoying than 500 kV transformer noise in terms of %HA and MA, which is related to different sharpness, roughness and tonality of the noises caused by transformers of the two voltage levels. Based on stepwise regression analysis, multiple linear regression models are further developed by using LAeq and roughness as acoustical parameters to predict the %HA and MA of transformer noise. Compared with the linear regression model that considered only LAeq values, multiple linear regressions can efficiently account for the different annoyance ratings of the two transformer noises at the same LAeq values.