Investigating multidimensional characteristics of noise signals with tones from building mechanical systems and their...

This paper investigates multidimensional characteristics of tonal noise from heating, ventilation, and air-conditioning systems, besides loudness and tonality, to improve prediction of annoyance. Two studies were conducted: multidimensional scaling (MDS) analysis to determine what other perceptual signal characteristics are important and perceptual weight analysis (PWA) to understand the impact of multiple tones in a signal. In the MDS study, paired comparison tasks were conducted to gather similarity and annoyance data. Results show that the latent perceptual dimensions are 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. Including both sharpness and tonal audibility does not further improve prediction performance, though. In the PWA study, noise stimuli with five-tone complexes between 125 Hz and 2 kHz were generated for subjective testing to obtain a perceptual weighting function. The levels of each tone were randomly adjusted for every trial, and both harmonic and inharmonic tone complexes were utilized. The PWA result was applied as a spectral weighting function to calculate a proposed weighted-sum tonal audibility metric. Utilizing the proposed metric instead of the traditional tonal audibility metric improves annoyance prediction to a similar degree as including sharpness.