{"title":"音频信号识别的可靠性评估","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":"{\"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}","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}
Reliability Evaluation of Audio Signal Recognition
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.