Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826766
Bipendra Basnyat, Nirmalya Roy, A. Gangopadhyay, A. Raglin
Flood is one of the common natural disasters that can severely affect human life and properties. Early detection, therefore, is of paramount importance to provide help through an emergency response team. Robust flood detection techniques so far have been based on computer vision using images either from cameras, satellite imagery, remote sensing, or radar-based images. However, sound signal-based flood event detection has not been widely explored. In this work, we design an end-to-end architecture for a deep learning-based flood-related sound event detection model. We employ Mel-Spectrogram-based auditory signal analysis and deep learning models for sound event detection (SED). We evaluated four deep learning models under the following two categories: (i) Binary classification Flood/No Flood, vs. Windy vs. Non-Windy, and (ii) Multi-classification for more granular flood and wind events. The experimental results performed in these settings on the datasets collected from real deployment showed an accuracy of around 78%.
{"title":"Environmental Sound Classification for Flood Event Detection","authors":"Bipendra Basnyat, Nirmalya Roy, A. Gangopadhyay, A. Raglin","doi":"10.1109/ie54923.2022.9826766","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826766","url":null,"abstract":"Flood is one of the common natural disasters that can severely affect human life and properties. Early detection, therefore, is of paramount importance to provide help through an emergency response team. Robust flood detection techniques so far have been based on computer vision using images either from cameras, satellite imagery, remote sensing, or radar-based images. However, sound signal-based flood event detection has not been widely explored. In this work, we design an end-to-end architecture for a deep learning-based flood-related sound event detection model. We employ Mel-Spectrogram-based auditory signal analysis and deep learning models for sound event detection (SED). We evaluated four deep learning models under the following two categories: (i) Binary classification Flood/No Flood, vs. Windy vs. Non-Windy, and (ii) Multi-classification for more granular flood and wind events. The experimental results performed in these settings on the datasets collected from real deployment showed an accuracy of around 78%.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131120477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-26DOI: 10.48550/arXiv.2203.13976
Qianyu Ou, Wenjun Zheng, Zhan Shi, Ruizhi Liao
Parking has been a painful problem for urban drivers. The parking pain exacerbates as more people tend to live in cities in the context of global urbanization. Thus, it is demanding to find a solution to mitigate drivers’ parking headaches. Many solutions tried to resolve the parking issue by predicting parking occupancy. Their focuses were on the accuracy of the theoretical side but lacked a standardized model to evaluate these proposals in practice. This paper develops a Driver-Side and Traffic-Based Evaluation Model (DSTBM), which provides a general evaluation scheme for different parking solutions. Two common parking detection methods - fixed sensing and mobile sensing - are analyzed using DSTBM. The results indicate: first, DSTBM examines different solutions from the driver’s perspective and has no conflicts with other evaluation schemes; second, DSTBM confirms that fixed sensing performs better than mobile sensing in terms of prediction accuracy.
{"title":"Driver-Side and Traffic-Based Evaluation Model for On-street Parking Solutions","authors":"Qianyu Ou, Wenjun Zheng, Zhan Shi, Ruizhi Liao","doi":"10.48550/arXiv.2203.13976","DOIUrl":"https://doi.org/10.48550/arXiv.2203.13976","url":null,"abstract":"Parking has been a painful problem for urban drivers. The parking pain exacerbates as more people tend to live in cities in the context of global urbanization. Thus, it is demanding to find a solution to mitigate drivers’ parking headaches. Many solutions tried to resolve the parking issue by predicting parking occupancy. Their focuses were on the accuracy of the theoretical side but lacked a standardized model to evaluate these proposals in practice. This paper develops a Driver-Side and Traffic-Based Evaluation Model (DSTBM), which provides a general evaluation scheme for different parking solutions. Two common parking detection methods - fixed sensing and mobile sensing - are analyzed using DSTBM. The results indicate: first, DSTBM examines different solutions from the driver’s perspective and has no conflicts with other evaluation schemes; second, DSTBM confirms that fixed sensing performs better than mobile sensing in terms of prediction accuracy.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129047900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：