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Perceived quality of speech degraded by wind noise: An assessment of sources of variability in a Web experiment
... Participants' word scores for each condition were analyzed in an 8 (wind noise level) Â 3 (gustiness) repeated-measures ANOVA. Greenhouse-Geisser correction estimates were used where the assumption of sphericity had been violated for the effect of wind level Significant effects were observed for wind level (p < 0.001, partial g 2 ¼ 0.61) and for the interaction of wind level and gustiness (p < 0.001, partial g 2 ¼ 0. 19). ...
... The four questions asked about: The sound reproduction equipment; how noisy the place where the experiment was being carried out was; the participant's age, and whether the participant considered themselves to be an audio expert. While significant differences were observed for many of the categories of participant information 19 it is notable that effect sizes were small across the board (all effect sizes < 0.017, partial g 2 ), relative to the effect size of the experimental wind level variable across the group (partial g 2 ¼ 0.30). ...
Wind can induce noise on microphones, causing problems for users of hearing aids and for those making recordings outdoors. Perceptual tests in the laboratory and via the Internet were carried out to understand what features of wind noise are important to the perceived audio quality of speech recordings. The average A-weighted sound pressure level of the wind noise was found to dominate the perceived degradation of quality, while gustiness was mostly unimportant. Large degradations in quality were observed when the signal to noise ratio was lower than about 15 dB. A model to allow an estimation of wind noise level was developed using an ensemble of decision trees. The model was designed to work with a single microphone in the presence of a variety of foreground sounds. The model outputted four classes of wind noise: none, low, medium, and high. Wind free examples were accurately identified in 79% of cases. For the three classes with noise present, on average 93% of samples were correctly assigned. A second ensemble of decision trees was used to estimate the signal to noise ratio and thereby infer the perceived degradation caused by wind noise.
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