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Related Experiment Video

Updated: Nov 30, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Modeling Evaluations of Low-Level Sounds in Everyday Situations Using Linear Machine Learning for Variable Selection.

Siegbert Versümer1, Jochen Steffens1, Patrick Blättermann1

  • 1Institute of Sound and Vibration Engineering, University of Applied Sciences Düsseldorf, Düsseldorf, Germany.

Frontiers in Psychology
|November 16, 2020
PubMed
Summary

Listener factors and situation context significantly impact low-level sound perception, influencing annoyance. The ability to mentally fade out sounds is key, alongside emotional states and situation positivity, in understanding soundscape effects.

Keywords:
Lassoenvironmental soundhuman perceptionmachine learningonline-surveysituationvariable selection

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Area of Science:

  • Psychoacoustics
  • Environmental Psychology
  • Machine Learning

Background:

  • Human sound perception is influenced by sound characteristics, listener factors, and situational context.
  • Low-level sounds, often overlooked, can significantly impact quality of life and health.
  • Understanding these factors is crucial for effective soundscape management and noise mitigation.

Purpose of the Study:

  • To investigate factors influencing the perception and annoyance of everyday low-level sounds.
  • To identify key situational and listener-related variables affecting sound perception.
  • To apply machine learning for robust modeling of sound perception and evaluation.

Main Methods:

  • An online study with 1,301 participants reporting on 2,800 everyday low-level sound situations.
  • Participants rated sound characteristics (loudness, timbre, tonality) and described situations using eight dimensions.
  • Percentile least absolute shrinkage and selection operator (Lasso) regularization was used for variable selection.

Main Results:

  • Annoyance ratings differed significantly across sound categories (natural, human, technical) for binary loudness levels.
  • The ability to mentally fade out sound emerged as a critical situational factor, alongside valence, arousal, and situation positivity/negativity.
  • Machine learning (Lasso) effectively addressed overfitting and multicollinearity in complex soundscape models.

Conclusions:

  • Low-level sound perception is complex, influenced by a combination of sound attributes, listener states, and situational context.
  • The developed machine learning approach provides a powerful tool for predicting sound perception and evaluation in future research.
  • This study offers a valuable database for advancing noise effects and soundscape research.