{"title":"Confidence measures for acoustic detection of film slates based on time-domain features","authors":"M. Schlosser","doi":"10.1109/ICASSP.2008.4517565","DOIUrl":null,"url":null,"abstract":"An acoustic detector for film slates is proposed to assist a human operator with the synchronization of audio and video in post-production. To be computationally efficient, the signal analysis is restricted to time-domain features. Although the features are statistically dependent, separate classifiers are trained for each of them. The statistical dependence is taken into account during the combination of the log-likelihood ratios provided by the individual classifiers. The overall confidence in a classification is determined as a weighted sum of the individual log-likelihood ratios, where the weights depend on the correlation between the different features. Experimental results for real-world recordings from film sets show that the confidence measures allow for a fast identification of the film slates while minimizing the interference from false detections.","PeriodicalId":333742,"journal":{"name":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2008.4517565","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
An acoustic detector for film slates is proposed to assist a human operator with the synchronization of audio and video in post-production. To be computationally efficient, the signal analysis is restricted to time-domain features. Although the features are statistically dependent, separate classifiers are trained for each of them. The statistical dependence is taken into account during the combination of the log-likelihood ratios provided by the individual classifiers. The overall confidence in a classification is determined as a weighted sum of the individual log-likelihood ratios, where the weights depend on the correlation between the different features. Experimental results for real-world recordings from film sets show that the confidence measures allow for a fast identification of the film slates while minimizing the interference from false detections.