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Advanced Acoustic Monitoring Using Psychoacoustic Heatmap Machine Learning Models for Noise Impact Prediction in

Kuen Wai Ma1,2, Cheuk Ming Mak2, Fu-Lai Chung3

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Summary
This summary is machine-generated.

This study introduces psychoacoustic heatmap machine learning models (PHMLM) to predict air conditioning sound quality and negative noise impacts. PHMLM-EPA models offer superior acoustic monitoring for sustainable building design.

Keywords:
acoustic monitoringmachine learningnoise impact predictionperceptual dimensions of soundspsychoacoustic heatmap

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Area of Science:

  • Acoustics
  • Machine Learning
  • Environmental Psychology

Background:

  • Air-conditioning systems are crucial for indoor environmental quality, but noise can detract from their benefits.
  • Acoustic monitoring is essential for managing noise pollution from HVAC systems.
  • Sound quality perception is understood through three psychological dimensions: Evaluation, Potency, and Activity (EPA).

Purpose of the Study:

  • To develop novel psychoacoustic heatmap machine learning models (PHMLM) for predicting sound quality.
  • To assess the negative noise impacts (Discomfortable, Annoying, Stressful, Unacceptable) of air conditioning sounds.
  • To evaluate the performance of PHMLM-EPA against traditional regression models (TRM) and the L_Aeq metric.

Main Methods:

  • Conducted 1208 jury listening tests with 101 participants on air-conditioned soundtracks.
  • Generated 227 × 227-pixel psychoacoustic heatmaps from time-varying psychoacoustic metrics (N, S, R, FS).
  • Utilized PHMLM-EPA models with psychoacoustic heatmaps as input for machine learning prediction.

Main Results:

  • PHMLMs achieved high predictive performance with correlation coefficients of 0.79 (E), 0.80 (P), and 0.62 (A).
  • PHMLM-EPA showed significantly better performance than TRM, with 31% lower mean absolute error and higher regression slope.
  • PHMLM-EPA demonstrated superior goodness-of-fit compared to TRM (+55% to +95%) and L_Aeq (+87% to +95%).

Conclusions:

  • Psychoacoustic heatmap machine learning models provide an advanced method for acoustic monitoring of air conditioning systems.
  • This approach enhances the prediction of sound quality and negative noise impacts.
  • The developed models support sustainable building designs by improving indoor environmental quality management.