{"title":"Reliability Evaluation of Audio Signal Recognition","authors":"E. Gershikov, Shiran Gabler","doi":"10.1109/INTERCON.2018.8526407","DOIUrl":null,"url":null,"abstract":"In this paper we propose reliability measures for algorithms that recognize an audio signal within a database of music recordings when only a short segment of it is available. Using these measures, we test the reliability of two algorithms: one based on spectrogram peaks and one based on MFCC features. We compare the performance of the two methods and conclude about the usability and usefulness of our evaluation.","PeriodicalId":305576,"journal":{"name":"2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)","volume":"78 26","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTERCON.2018.8526407","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper we propose reliability measures for algorithms that recognize an audio signal within a database of music recordings when only a short segment of it is available. Using these measures, we test the reliability of two algorithms: one based on spectrogram peaks and one based on MFCC features. We compare the performance of the two methods and conclude about the usability and usefulness of our evaluation.
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