2013
DOI: 10.1121/1.4820890
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Echo thresholds for reflections from acoustically diffusive architectural surfaces

Abstract: When sound reflects from an irregular architectural surface, it spreads spatially and temporally. Extensive research has been devoted to prediction and measurement of diffusion, but less has focused on its perceptual effects. This paper examines the effect of temporal diffusion on echo threshold. There are several notable differences between the waveform of a reflection identical to the direct sound and one from an architectural surface. The onset and offset are damped and the energy is spread in time; hence, … Show more

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Cited by 17 publications
(17 citation statements)
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“…Especially in enclosed spaces, humans are quite sensitive to the perception of sound in all its temporal, spectral, and spatial aspects, which makes a realistic auralization in room acoustics quite challenging. These three aspects are strongly affected by the presence of diffusive surfaces [36], thus more insight into their use is needed.…”
Section: Introductionmentioning
confidence: 99%
“…Especially in enclosed spaces, humans are quite sensitive to the perception of sound in all its temporal, spectral, and spatial aspects, which makes a realistic auralization in room acoustics quite challenging. These three aspects are strongly affected by the presence of diffusive surfaces [36], thus more insight into their use is needed.…”
Section: Introductionmentioning
confidence: 99%
“…An energy interpretation was successful in the work of Robinson et al on localization of diffuse reflections [15] and in [25] to model primary auditory events caused by multiple loudspeakers. Consequently, a vector approach was also used in [17] to model the orientation-dependent localization of directional sound sources in a room, which we largely adopt here.…”
Section: Modeling the Primary Localizationmentioning
confidence: 99%
“…(8) The precedence threshold given in [3,15] decays by minus 1 4 dB/ms. In this paper we use the threshold raised by a factor of two to predict our experiment well:…”
Section: Modeling the Primary Localizationmentioning
confidence: 99%
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