VF‐EFENet: A novel method for environmental sound filtering and feature extraction

Xiao, Hong; Ma, Zongxin; Jiang, Wenchao; Cao, Xianglin; Fan, Yuquan; Wang, Hao

doi:10.1002/int.23010

Cited by 1 publication

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References 24 publications

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“…In the Mel frequency plot, the slope of the curve becomes gradually smaller as the frequency increases, which indicates that the human ear is more sensitive to the perception of low-frequency audio signals [32]. In the Mel frequency domain, the human perception of pitch is linear, and the calculation flow of MFCC is shown in Fig.…”

Section: ) Tourism Audio Feature Extractionmentioning

confidence: 99%

The Application of Multimodal Discourse Analysis in Urban Intercultural Communication

Chen

2023

Applied Mathematics and Nonlinear Sciences

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This paper firstly analyzes the basic composition of intercultural communication ability, discusses the specific function of cultural promotion of urban tourism, and gives the promotion effect of intercultural communication on urban tourism. Secondly, the connotation of multimodal discourse analysis is given, and the technical analysis of text, audio, and visual features is carried out for the analysis of urban tourism discourse. Then, the TF-IDF algorithm is used to realize the feature extraction of tourism culture text, the MFCC algorithm is used to extract the audio features of tourism culture, and the modal classification network is used to realize the recognition of the visual features of urban tourism culture video. Finally, to verify the effectiveness of the application of multimodal discourse analysis in urban cross-cultural communication, three aspects were tested and analyzed respectively. The results show that the F1 value of the TF-IDF algorithm is 0.912, which is 17.07% higher than that of the CTF-TF-IDF algorithm. When the amount of audio recognition is 5GB, the recognition time of the MFCC audio recognition method is 10.4 s. When the weight value of the visual feature extraction network is set to 1.0, the highest visual feature extraction error rate is only 3.96%. Using multimodal discourse analysis to carry out urban tourism discourse analysis can realize more comprehensive urban tourism feature extraction, help tourists strengthen their tourism perception, and then promote the enhancement of urban cross-cultural communication ability.

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Section: ) Tourism Audio Feature Extractionmentioning

confidence: 99%

The Application of Multimodal Discourse Analysis in Urban Intercultural Communication

Chen

2023

Applied Mathematics and Nonlinear Sciences

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VF‐EFENet: A novel method for environmental sound filtering and feature extraction

Abstract: To solve the problems such as low accuracy and low retrieval performance in feature extraction of environmental sound data from Internet consumer finance scenario, a novel method for environmental sound filtering and feature extraction (VF-EFENet)

Cited by 1 publication

References 24 publications

The Application of Multimodal Discourse Analysis in Urban Intercultural Communication

The Application of Multimodal Discourse Analysis in Urban Intercultural Communication

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