Sound quality metric indicators of rotorcraft noise annoyance using multilevel regression analysis

Boucher, Matthew Alban; Krishnamurthy, Siddhartha; Christian, Andrew; Rizzi, Stephen A.

doi:10.1121/2.0001223

Cited by 11 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sharpness has been found to be an important contributor to aircraft noise annoyance . Sharpness, tonality and fluctuation strength were found to be important predictors of annoyance for rotorcraft-like sounds Boucher et al, 2020). Roughness has been found, for instance, an important factor to describing sound quality of electric motors (Mosquera-Sanchez et al, 2014;Ercan, 2019).…”

Section: Models For Psychoacoustic Annoyancementioning

confidence: 99%

“…EPNL or SEL) in predicting noise annoyance of fixed-wing aircraft (Rizzi et al, 2016;. Recently, several authors Boucher et al, 2020) (2009). This sharpness method does not take into account the influence of absolute loudness on the sharpness perception.…”

Section: Psychoacoustic Data Analysismentioning

confidence: 99%

“…Sottek's hearing model simulates the signal processing of human hearing and accounts for its limitations to track fast temporal changes within a critical band (Boucher et al, 2020). Tonality was calculated according to Aures/Terhardt tonality model (Aures, 1985b).…”

Section: Psychoacoustic Data Analysismentioning

confidence: 99%

“…The impulsiveness (measured in IU) of all sound samples were calculated using the Sottek's hearing model. This psychoacoustic metric accounts for the perception caused by short and sudden changes in sound pressure level (Boucher, et al, 2019). A full description of the calculation of the impulsiveness metrics and its computation in the Sottek's hearing model can be found at Sottek et al (1995) and Sottek and Genuit (2005) respectively.…”

Section: Psychoacoustic Data Analysismentioning

confidence: 99%

“…Fluctuation strength and roughness describe how slow and rapid fluctuations, respectively, of the sound level are perceived. The psychoacoustic metrics sharpness, tonality and fluctuation strength have been suggested as good indicators of rotorcraft noise annoyance Boucher et al, 2020). Investigating the performance of different psychoacoustic metrics to account for the perception of different aspects of aircraft noise, Barbot et al (2008) found fluctuation strength as a good indicator of perceptual effects of turbulence and sharpness as a good indicator of the perceptual effects of high frequency noise.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Psychoacoustic analysis of contra-rotating propeller noise for unmanned aerial vehicles

Torija

Paruchuri

2021

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV) technologies are rapidly advancing due to the unlimited number of applications from parcel delivery to people transportation. As the UAV market expands, community noise impact will become a significant problem for public acceptance. Compact drone architectures based on contra-rotating propellers bring significant benefits in terms of aerodynamic performance and redundancy to ensure vehicle control in case of component failure. However, contra-rotating propellers are severely noisy if not designed appropriately. In the framework of a perception-influenced design approach, this paper investigates the optimal rotor spacing distance configuration to minimise noise annoyance. On the basis of a series of psychoacoustic metrics (i.e. loudness, fluctuation strength, roughness, sharpness and tonality) and psychoacoustic annoyance models, the optimal rotor axial separation distance (expressed as a function of propeller blade diameter) is at a range of 0.2 to 0.4. This paper also discusses the performance of currently available psychoacoustic models to predict propeller noise annoyance, and defines further work to develop a psychoacoustic annoyance model optimised for rotating systems.

show abstract

Section: Models For Psychoacoustic Annoyancementioning

confidence: 99%

Section: Psychoacoustic Data Analysismentioning

confidence: 99%

Section: Psychoacoustic Data Analysismentioning

confidence: 99%

Section: Psychoacoustic Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations