Determination of preference-equivalent levels for fan noise and their prediction by indices based on specific loudness patterns

The influence of the reference level on loudness and preference judgements for spectrally manipulated fan sounds

Fan sounds are often quantified using A-weighted sound pressure levels, silently acknowledging their limitations to fully capture the perceived unpleasantness. To overcome this limitation, level adjustments are a way to quantify the subjective preference of spectrally different sounds in listening experiments by adjusting the level of a test sound until it is equally preferred to a fixed reference sound. Since equal loudness contours differ, depending on the overall level, level adjustments might vary for different levels of the reference sound. This study aims to quantify the effects of spectral manipulations on level adjustments for loudness and preference judgements at reference sound pressure levels of either 45, 60, or 75 dB(A). Level adjustments of up to 12 dB were measured to make the stimuli equally preferred to the reference, particularly for sounds with prominent high-frequency components. The loudness and preference judgements were closely linked with each other, but an offset of about 3.5 dB at a reference level of 45 dB(A) indicates that equal loudness is not synonymous with equal preference. A linear regression model to predict level adjustments based on the reference level and an index reflecting the ratio of mid- to high-frequency loudness explains 73% of the variance.

Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction

Through user characteristic information, user interaction behavior, commodity characteristic information, recommendation engine, and related technologies in data mining, this paper makes a more in-depth study, and analyzes the problems of "big data volume", "cold start" and "data sparsity" in the recommender system in modern business websites. In response to these problems, this paper transforms the problem of large data volume into the problem of large user groups. Then, after using the k-means clustering algorithm to divide the large user group into homogeneous user groups to alleviate the problem, a combination of collaborative filtering algorithm and content-based recommendation algorithm in the homogeneous user group is proposed to alleviate this problem. The experimental precision and recall are both around 0.4, and when W=0.8, the F value is the largest.

Loudness and short-term annoyance of sonic boom signatures at low levels

Supersonic aircraft produce a sonic boom when flying faster than the speed of sound. To rule out detrimental effects for inhabitants of overflown areas, civil supersonic flights (like the Concorde) were only allowed to fly at supersonic speed when over water. Due to the progress in aircraft design, the supersonic boom may be reduced considerably in the future. In this study, listening tests were carried out with a variety of low boom simulations and conventional sonic boom signatures in a similar level range. Participants rated the loudness and the short-term annoyance of 24 sonic boom signals, which differed in terms of the signature shape and maximum pressure but were confined to a range of A-weighted sound exposure levels around 60 dB(A). The results showed main effects of signature and relative level variation as well as an interaction of the two. Correlation coefficients between the ratings and sound exposure levels were highest for A-weighted sound exposure levels compared to other frequency weightings. Contrary to our expectations, the provision of background information about the nature of the presented sound sources had no statistically significant influence on the ratings.