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Auditory distraction in open-plan office environments: The effect of multi-talker acoustics

2023-04-14 · Manuj Yadav, Jungsoo Kim, Densil Cabrera, Richard de Dear

Within the soundscapes of open-plan offices, irrelevant speech has consistently been reported as the most distracting, and causing performance decrements for workers. Notwithstanding this generalization, the 'babble' created by multiple simultaneously active talkers can sometimes provide beneficial sound masking, but due to spatial release from masking (SRM), speech may still be sufficiently intelligible up to a certain number of talkers (estimated to be about four). This was explored within a highly-realistic office simulation, where the cognitive performance, and subjective distraction of participants were tested. The experimental design was a 4x2 factorial (4 talker numbers, 2 levels of broadband sound masking, as the factors). The results indicated that within lower sound pressure level (SPL) of broadband sound masking, multi-talker sound environments degraded cognitive tasks performance more than those with a single talker, suggesting SRM effects. For higher SPL broadband sound masking, the cognitive test scores were similar within the different talker numbers. The subjective distraction increased monotonically with the number of talkers, with higher distraction within lower SPL broadband sound masking. Overall, the results call into question the single talker assumption (being the most distracting) within the international standard for measuring open-plan office acoustic environments (ISO 3382-3:2012). Soundscapes with 4 simultaneous talkers were still not adequately providing beneficial 'babble' masking, and were more distracting than 1 active talker. In conclusion, it is suggested that the acoustics environment of open-plan offices needs better characterization by incorporating some of the complexity and psychoacoustics of multi-talker scenarios.

📄 PDF Abstract BibTeX arXiv:2304.12817

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SRM SRM combines style transfer with an attention mechanism. Its main contribution is style pooling which utilizes both mean and standard deviation of the input features to improve…

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