title: Comment on "Short-term acoustic forecasting via artificial neural networks for neonatal intensive care units" [J. Acoust. Soc. Am. 132, 3234-3239 (2012)].
abstract: In contrast to common expectations, the noise levels measured in hospital wards are known to be high with little day-night variation, potentially having negative effects on the patient outcomes and the work performance of the staff members, and considerable research attention has been drawn to such adverse acoustic conditions in healthcare environments. Recently, Young et al. [J. Acoust. Soc. Am. 132(5), 3234-3239 (2012)] proposed to use an artificial neural network (ANN) to predict the hourly energy-equivalent sound pressure level (Leq, 1h), particularly targeting neonatal intensive care units. Despite the timeliness of the study and the potential benefits of an "acoustic forecasting" model, the proposed scheme appears to be underdeveloped in a few important aspects, which this letter attempts to address. In particular, the prediction of a simpler time-series smoothing technique was equally or more accurate compared to that of the ANN. In addition, the percentage error used to indicate the prediction accuracy was not only perceptually irrelevant but also misleading given the narrow distribution of test data. Furthermore, this letter raises the more general question whether the sound pressure level may meaningfully be modeled solely based on the past time-series.
authors.affiliation: Philips Research Laboratories, High Tech Campus 36, 5656 AE Eindhoven, The Netherlands. mun.park@philips.com
authors.forename: Munhum
authors.initials: M
authors.lastname: Park
mesh_terms: D000162:Acoustics; D004781:Environmental Exposure; D006749:Hospital Design and Construction / Q000379:methods*; D006801:Humans; D007363:Intensive Care Units, Neonatal; D016571:Neural Networks, Computer; D009622:Noise