{"title":"广播新闻中音频事件的分类","authors":"Zhu Liu, Qian Huang","doi":"10.1109/MMSP.1998.738963","DOIUrl":null,"url":null,"abstract":"This paper describes an approach to discriminate news report from others such as commercials and music in broadcast news programs based on audio information. The reported work is part of the effort at AT&T to hierarchically segment broadcast news programs into semantically meaningful units at different levels of abstraction. At the coarse level, using the described approach we preprocess the audio data to pass only the news segments as input to a speaker identification system. To develop a lightweight preprocessing scheme for efficiency, we adopted a set of audio features that are simple to compute yet, based on our observation, statistically capture the intrinsic properties of the audio events to be classified. To improve the performance of the classifier, fuzzy membership functions associated with the features are introduced. Preliminary experimental results are reported which demonstrate the usefulness of the approach.","PeriodicalId":180426,"journal":{"name":"1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"Classification of audio events in broadcast news\",\"authors\":\"Zhu Liu, Qian Huang\",\"doi\":\"10.1109/MMSP.1998.738963\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes an approach to discriminate news report from others such as commercials and music in broadcast news programs based on audio information. The reported work is part of the effort at AT&T to hierarchically segment broadcast news programs into semantically meaningful units at different levels of abstraction. At the coarse level, using the described approach we preprocess the audio data to pass only the news segments as input to a speaker identification system. To develop a lightweight preprocessing scheme for efficiency, we adopted a set of audio features that are simple to compute yet, based on our observation, statistically capture the intrinsic properties of the audio events to be classified. To improve the performance of the classifier, fuzzy membership functions associated with the features are introduced. Preliminary experimental results are reported which demonstrate the usefulness of the approach.\",\"PeriodicalId\":180426,\"journal\":{\"name\":\"1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMSP.1998.738963\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMSP.1998.738963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper describes an approach to discriminate news report from others such as commercials and music in broadcast news programs based on audio information. The reported work is part of the effort at AT&T to hierarchically segment broadcast news programs into semantically meaningful units at different levels of abstraction. At the coarse level, using the described approach we preprocess the audio data to pass only the news segments as input to a speaker identification system. To develop a lightweight preprocessing scheme for efficiency, we adopted a set of audio features that are simple to compute yet, based on our observation, statistically capture the intrinsic properties of the audio events to be classified. To improve the performance of the classifier, fuzzy membership functions associated with the features are introduced. Preliminary experimental results are reported which demonstrate the usefulness of the approach.
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