Michael J. B. Lotinga

Psychoacoustic metrics offer the means to analyse sound qualities using sophisticated models of human perception. A substantial drawback is the need for high-resolution acoustic data as input. Quasi-psychoacoustic metrics are proposed to address sound quality analysis when high-resolution acoustic data are unavailable. The proposed metrics aim to approximate sound qualities of loudness, sharpness, and tonal loudness using acoustic data with a much lower time resolution. As such, the metrics are expected to be primarily applicable to sounds featuring relatively slow modulations in intensity. A particular application case is used to validate the metrics: sound quality predictions for unmanned aircraft systems.

The Sottek Hearing Model provides a means to analyse perceptual sound qualities including loudness, tonality, roughness, and fluctuation strength. An extension to the loudness algorithms is described, which addresses cases in which a source of interest is partially masked by other sounds, such as an encompassing ambient acoustic environment. The proposed extension is validated with objective predictions for unmanned aircraft systems (UAS) and artificial noise, and subjective test results for UAS. The partial loudness extension proposed also enables the calculation of further partial sound qualities, including partial tonality, partial tonal loudness, and partial sharpness. Software implementations of the tools are provided.

This study explores the soundscape impact of unmanned aircraft systems (UAS) noise in an urban park using soundwalks in two urban park areas: one with prominent traffic noise from a nearby thoroughfare, and the other behind a row of tall pine trees with much diminished traffic noise and more prominent sounds of nature. Objective measurements and subjective responses reveal that takeoff and landing events have greater impact than flyovers, with stronger effects at lower altitudes. UAS noise significantly affects pleasantness and eventfulness in the quieter area, while the noisier area shows smaller perceptual changes. Despite greater differences in the quieter area, pleasantness levels remained higher than in the noisier, suggesting a compensatory effect of ambient noise. Detectability and tonality negatively correlated with pleasantness changes, while impulsiveness correlated positively with absolute pleasantness, likely due to ambient scene characteristics rather than UAS noise itself. The study highlights the importance of considering the background ambience in moderating the perception of UAS noise.

Unmanned aircraft systems (UAS) are emerging for use in civil applications such as commercial logistics, surveying, agriculture, and maintenance tasks. One challenge raised by this technology is to understand how humans respond to UAS sound, the characteristics of which can be varied and unfamiliar, compared with conventional vehicles. Efforts are under way to facilitate flight path planning and optimisation incorporating noise annoyance prediction models. Coupled with UAS sound emission and propagation models, perception and response could be predicted using acoustic and psychoacoustic metrics found to be associated with subjective evaluation. However, identifying the most effective metrics and models is complicated by the wide array of possible descriptors. In this study, a multi-stage modelling approach was developed. This combined a flexible, non-parametric machine learning technique to identify acoustic and psychoacoustic metrics associated with noise annoyance responses to UAS sound, obtained experimentally within immersive audio scenes. This information was used to develop semi-parametric models to predict responses while addressing cluster-correlation in the data. This technique is contrasted with an alternative multilevel, mixed-effects regression approach to highlight the potential advantages. The prediction models also demonstrate how sound intensity, ambient acoustic environments, sound qualities, and number of flights affect UAS noise annoyance.

The primary purpose of this letter is to offer historical clarification on the development of a model for “psychoacoustic annoyance” as cited by Di, Chen, Li, and Li [J. Acoust. Soc. Am. 156(3), 1877–1886 (2024)], to raise awareness of its origin and propose a more appropriate attribution. A secondary aspect concerns the notions of noise annoyance advanced by Di, Chen, Li, and Li [J. Acoust. Soc. Am. 156(3), 1877–1886 (2024)], addressing associated semantic and conceptual matters, with the aim of stimulating debate and in the hope of advancing towards future consensus.

The operation of jet fans to ventilate tunnels generates sound that propagates within the tunnels, and can be emitted to external locations, which may include noise-sensitive receptors. To achieve suitable predictions of noise impact, it is important to account for the propagation of sound inside tunnels, which is an engineering problem not addressed by current standards or codes of practice. A study was conducted to review existing approaches and determine a suitable model to predict tunnel sound propagation. Theoretical approaches were reviewed, and a 3D geometrical model was generated, the predictions of which were validated using empirical data. A simpler modelling approach identified from the literature was compared with the 3D model, and the differences observed were analysed. The study concludes that the simple model could be used to obtain reasonably accurate estimations, which can be made conservative by applying adjustments to the predicted sound levels.