{"title":"Classification and mapping of sound sources in local urban streets through AudioSet data and Bayesian optimized Neural Networks","authors":"Deepank Verma, Arnab Jana, K. Ramamritham","doi":"10.1515/noise-2019-0005","DOIUrl":null,"url":null,"abstract":"Abstract Deep learning (DL) methods have provided several breakthroughs in conventional data analysis techniques, especially with image and audio datasets. Rapid assessment and large-scale quantification of environmental attributes have been possible through such models. This study focuses on the creation of Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) based models to classify sound sources from manually collected sound clips in local streets. A subset of an openly available AudioSet data is used to train and evaluate the model against the common sound classes present in the urban streets. The collection of audio data is done at random locations in the selected study area of 0.2 sq. km. The audio clips are further classified according to the extent of anthropogenic (mainly traffic), natural and human-based sounds present in particular locations. Rather than the manual tuning of model hyperparameters, the study utilizes Bayesian Optimization to obtain hyperparameter values of Neural Network models. The optimized models produce an overall accuracy of 89 percent and 60 percent on the evaluation set for three and fifteen-class model respectively. The model detections are mapped in the study area with the help of the Inverse Distance Weighted (IDW) spatial interpolation method.","PeriodicalId":44086,"journal":{"name":"Noise Mapping","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/noise-2019-0005","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Noise Mapping","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/noise-2019-0005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 12
Abstract
Abstract Deep learning (DL) methods have provided several breakthroughs in conventional data analysis techniques, especially with image and audio datasets. Rapid assessment and large-scale quantification of environmental attributes have been possible through such models. This study focuses on the creation of Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) based models to classify sound sources from manually collected sound clips in local streets. A subset of an openly available AudioSet data is used to train and evaluate the model against the common sound classes present in the urban streets. The collection of audio data is done at random locations in the selected study area of 0.2 sq. km. The audio clips are further classified according to the extent of anthropogenic (mainly traffic), natural and human-based sounds present in particular locations. Rather than the manual tuning of model hyperparameters, the study utilizes Bayesian Optimization to obtain hyperparameter values of Neural Network models. The optimized models produce an overall accuracy of 89 percent and 60 percent on the evaluation set for three and fifteen-class model respectively. The model detections are mapped in the study area with the help of the Inverse Distance Weighted (IDW) spatial interpolation method.
期刊介绍:
Ever since its inception, Noise Mapping has been offering fast and comprehensive peer-review, while featuring prominent researchers among its Advisory Board. As a result, the journal is set to acquire a growing reputation as the main publication in the field of noise mapping, thus leading to a significant Impact Factor. The journal aims to promote and disseminate knowledge on noise mapping through the publication of high quality peer-reviewed papers focusing on the following aspects: noise mapping and noise action plans: case studies; models and algorithms for source characterization and outdoor sound propagation: proposals, applications, comparisons, round robin tests; local, national and international policies and good practices for noise mapping, planning, management and control; evaluation of noise mitigation actions; evaluation of environmental noise exposure; actions and communications to increase public awareness of environmental noise issues; outdoor soundscape studies and mapping; classification, evaluation and preservation of quiet areas.