Yong Tang, Anqin Lu, Z. Liu, Y. Leng, Rongyan Wang, Chengli Sun, Jiande Sun, Chan Lin, Weiwei Zhao, Wenjing Li
{"title":"基于稀疏编码和卷积神经网络的声场景分类","authors":"Yong Tang, Anqin Lu, Z. Liu, Y. Leng, Rongyan Wang, Chengli Sun, Jiande Sun, Chan Lin, Weiwei Zhao, Wenjing Li","doi":"10.1109/IC-NIDC54101.2021.9660528","DOIUrl":null,"url":null,"abstract":"CNN is a model which is currently widely used in acoustic scene classification. Sparse coding is a model which used to be very popular in acoustic classification field before deep learning technology is widely used. In this paper we combine these two models for acoustic scene classification. Specifically, the calibrated sparse representation based score is fused with the score obtained through CNN classification model for classification. Experimental results on TUT acoustic scenes 2017 dataset and LITIS Rouen dataset show that the proposed algorithm can make good use of the classification abilities of sparse coding and CNN.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Acoustic Scene Classification Based on Sparse Coding and Convolutional Neural Networks\",\"authors\":\"Yong Tang, Anqin Lu, Z. Liu, Y. Leng, Rongyan Wang, Chengli Sun, Jiande Sun, Chan Lin, Weiwei Zhao, Wenjing Li\",\"doi\":\"10.1109/IC-NIDC54101.2021.9660528\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"CNN is a model which is currently widely used in acoustic scene classification. Sparse coding is a model which used to be very popular in acoustic classification field before deep learning technology is widely used. In this paper we combine these two models for acoustic scene classification. Specifically, the calibrated sparse representation based score is fused with the score obtained through CNN classification model for classification. Experimental results on TUT acoustic scenes 2017 dataset and LITIS Rouen dataset show that the proposed algorithm can make good use of the classification abilities of sparse coding and CNN.\",\"PeriodicalId\":264468,\"journal\":{\"name\":\"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC-NIDC54101.2021.9660528\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC-NIDC54101.2021.9660528","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Acoustic Scene Classification Based on Sparse Coding and Convolutional Neural Networks
CNN is a model which is currently widely used in acoustic scene classification. Sparse coding is a model which used to be very popular in acoustic classification field before deep learning technology is widely used. In this paper we combine these two models for acoustic scene classification. Specifically, the calibrated sparse representation based score is fused with the score obtained through CNN classification model for classification. Experimental results on TUT acoustic scenes 2017 dataset and LITIS Rouen dataset show that the proposed algorithm can make good use of the classification abilities of sparse coding and CNN.