The Effect of Low Frequency Noise on Working Speed and  Annoyance

Javadi, Anis; Pourabdian, Siamak; Forouharmajd, Farhad

doi:10.18502/ijph.v51i11.11184

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…This suggests that responses to LFN within the domain of higher-order cognitive functions are relatively consistent. Previous studies have demonstrated that low-frequency traffic noise slows down reading speed and negatively impacts performance in mathematical tasks [ 48 ] and that LFN can reduce mental arithmetic test accuracy [ 47 ]. Gao et al [ 51 ] observed a significant longitudinal negative correlation between traffic noise exposure and mathematical performance in cognitive testing.…”

Section: Discussionmentioning

confidence: 99%

Effect of low-frequency noise exposure on cognitive function: a systematic review and meta-analysis

Liang,

Li,

et al. 2024

BMC Public Health

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Background Low-frequency noise may cause changes in cognitive function. However, there is no established consensus on the effect of low-frequency noise on cognitive function. Therefore, this systematic review and meta-analysis aimed to explore the relationship between low-frequency noise exposure and cognitive function. Methods We conducted a systematic review and identified original studies written in English on low-frequency noise and cognition published before December 2022 using the PsycINFO, PubMed, Medline, and Web of Science databases. The risk of bias was evaluated according to established guidelines. A random-effects meta-analysis was performed where appropriate. To explore the association between low-frequency noise exposure and cognitive function, we reviewed eight relevant studies. These studies covered cognitive functions grouped into four domains: attention, executive function, memory, and higher-order cognitive functions. The data extraction process was followed by a random-effects meta-analysis for each domain, which allowed us to quantify the overall effect. Results Our analysis of the selected studies suggested that interventions involving low-frequency noise only had a negative impact on higher-order cognitive functions (Z = 2.42, p = 0.02), with a standardized mean difference of -0.37 (95% confidence interval: -0.67, -0.07). A moderate level of heterogeneity was observed among studies (p = 0.24, I2 = 29%, Tau2 = 0.03). Conclusions Our study findings suggest that low-frequency noise can negatively impact higher-order cognitive functions, such as logical reasoning, mathematical calculation, and data processing. Therefore, it becomes important to consider the potential negative consequences of low-frequency noise in everyday situations, and proactive measures should be taken to address this issue and mitigate the associated potential adverse outcomes.

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Section: Discussionmentioning

confidence: 99%

Effect of low-frequency noise exposure on cognitive function: a systematic review and meta-analysis

Liang,

Li,

et al. 2024

BMC Public Health

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“…Research on low-frequency sound (LFS), ranging from 15 Hz to 120 Hz, has primarily focused on low-frequency noise (LFN) [1,2], particularly the negative impact of LFS on human emotion perception or behaviors [3][4][5], e.g., the sense of noise in LFS is the main factor that incurs the perception of annoyance. Experimental comparisons have shown that LFS is more strongly associated with annoyance than other types under the same sound pressure level [6].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals

Wan

et al. 2023

Applied Sciences

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Since informatization and digitization came into life, audio signal emotion classification has been widely studied and discussed as a hot issue in many application fields. With the continuous development of artificial intelligence, in addition to speech and music audio signal emotion classification technology, which is widely used in production life, its application is also becoming more and more abundant. Current research on audiovisual scene emotion classification mainly focuses on the frame-by-frame processing of video images to achieve the discrimination of emotion classification. However, those methods have the problems of algorithms with high complexity and high computing cost, making it difficult to meet the engineering needs of real-time online automatic classification. Therefore, this paper proposes an automatic algorithm for the detection of effective movie shock scenes that can be used for engineering applications by exploring the law of low-frequency sound effects on the perception of known emotions, based on a database of movie emotion scene clips in 5.1 sound format, extracting audio signal feature parameters and performing dichotomous classification of shock and other types of emotions. As LFS can enhance a sense of shock, a monaural algorithm for detecting emotional scenes with impact using a subwoofer (SW) is proposed, which trained a classification model using SW monaural features and achieved a maximum accuracy of 87% on the test set using a convolutional neural network (CNN) model. To expand the application scope of the above algorithm, a monaural algorithm for detecting emotional scenes with impact based on low-pass filtering (with a cutoff frequency of 120 Hz) is proposed, which achieved a maximum accuracy of 91.5% on the test set using a CNN model.

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The Effect of Low Frequency Noise on Working Speed and Annoyance

Abstract: The Article Abstract is not available.

Cited by 2 publications

References 2 publications

Effect of low-frequency noise exposure on cognitive function: a systematic review and meta-analysis

Effect of low-frequency noise exposure on cognitive function: a systematic review and meta-analysis

Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals

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