{"title":"基于时间信息和音频特征的主题模型流行音乐估计","authors":"S. Kinoshita, Takahiro Ogawa, M. Haseyama","doi":"10.1109/GCCE.2014.7031200","DOIUrl":null,"url":null,"abstract":"This paper presents popular music estimation based on a topic model using time information and audio features. The proposed method calculates latent topic distribution using Latent Dirichlet Allocation to obtain more accurate music features. In this approach, we also use release date information of each music as time information for concerning the relationship between music trends and each age. Then, by using the obtained latent topic distribution features, the estimation of the popular music becomes feasible based on a Support Vector Machine classifier. Experimental results show the effectiveness of our method.","PeriodicalId":145771,"journal":{"name":"2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Popular music estimation based on topic model using time information and audio features\",\"authors\":\"S. Kinoshita, Takahiro Ogawa, M. Haseyama\",\"doi\":\"10.1109/GCCE.2014.7031200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents popular music estimation based on a topic model using time information and audio features. The proposed method calculates latent topic distribution using Latent Dirichlet Allocation to obtain more accurate music features. In this approach, we also use release date information of each music as time information for concerning the relationship between music trends and each age. Then, by using the obtained latent topic distribution features, the estimation of the popular music becomes feasible based on a Support Vector Machine classifier. Experimental results show the effectiveness of our method.\",\"PeriodicalId\":145771,\"journal\":{\"name\":\"2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCCE.2014.7031200\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCCE.2014.7031200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Popular music estimation based on topic model using time information and audio features
This paper presents popular music estimation based on a topic model using time information and audio features. The proposed method calculates latent topic distribution using Latent Dirichlet Allocation to obtain more accurate music features. In this approach, we also use release date information of each music as time information for concerning the relationship between music trends and each age. Then, by using the obtained latent topic distribution features, the estimation of the popular music becomes feasible based on a Support Vector Machine classifier. Experimental results show the effectiveness of our method.