Applying unsupervised machine learning clustering techniques to hospital soundscapes

Kenton Hummel, Erica Ryherd, Bethany Lowndes

Research output: Contribution to journalConference articlepeer-review


Noise in hospitals can be problematic for both patients and staff and is consistently rated poorly on national patient satisfaction surveys. Previous studies have linked negative outcomes of hospital noise to numerous patient and staff challenges, such as reduced sleep and disrupted communication. Existing articles and guidelines commonly use equivalent sound pressure level as a primary noise metric. Additional insights into typical sound levels experienced by occupants can be found through more detailed statistical analyses of sound, such as by applying unsupervised machine learning clustering techniques. Clustering techniques are applied in an effort to provide a more detailed analysis of the soundscape and various patterns of room activity. Noise data collected in three adult, inpatient hospital units were analyzed using clustering techniques and compared against patient satisfaction scores. This more thorough, statistical characterization of the hospital soundscape can lead to better understanding of patterns of noise conditions and resultant occupant perceptions.

Original languageEnglish (US)
Article number15002
JournalProceedings of Meetings on Acoustics
Issue number1
StatePublished - May 23 2022
Event182nd Meeting of the Acoustical Society of America, ASA 2022 - Denver, United States
Duration: May 23 2022May 27 2022

ASJC Scopus subject areas

  • Acoustics and Ultrasonics


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