%Aigaion2 BibTeX export from Idiap Publications
%Saturday 21 December 2024 05:32:37 PM

@INPROCEEDINGS{Korchagin_AC_2009,
         author = {Korchagin, Danil},
       keywords = {confidence estimation, pattern matching, time-frequency analysis},
       projects = {Idiap, TA2},
          month = {11},
          title = {Out-of-Scene AV Data Detection},
      booktitle = {Proceedings IADIS International Conference Applied Computing},
         volume = {2},
           year = {2009},
       location = {Rome, Italy},
        address = {P.O. Box 592, CH-1920 Martigny, Switzerland},
           isbn = {978-972-8924-97-3},
       crossref = {Korchagin_Idiap-RR-31-2009},
       abstract = {In this paper, we propose a new approach for the automatic audio-based out-of-scene detection of audio-visual data, recorded by different cameras, camcorders or mobile phones during social events. All recorded data is clustered to out-of-scene and in-scene datasets based on confidence estimation of cepstral pattern matching with a common master track of the event, recorded by a reference camera. The core of the algorithm is based on perceptual time-frequency analysis and confidence measure based on distance distribution variance. The results show correct clustering in 100\% of cases for a real life dataset and surpass the performance of cross correlation while keeping lower system requirements.},
            pdf = {https://publications.idiap.ch/attachments/papers/2009/Korchagin_AC_2009.pdf}
}



crossreferenced publications: 
@TECHREPORT{Korchagin_Idiap-RR-31-2009,
         author = {Korchagin, Danil},
       keywords = {confidence estimation, pattern matching, time-frequency analysis},
       projects = {Idiap, TA2},
          month = {11},
          title = {Out-of-Scene AV Data Detection},
           type = {Idiap-RR},
         number = {Idiap-RR-31-2009},
           year = {2009},
    institution = {Idiap},
        address = {P.O. Box 592, CH-1920 Martigny, Switzerland},
       abstract = {In this paper, we propose a new approach for the automatic audio-based out-of-scene detection of audio-visual data, recorded by different cameras, camcorders or mobile phones during social events. All recorded data is clustered to out-of-scene and in-scene datasets based on confidence estimation of cepstral pattern matching with a common master track of the event, recorded by a reference camera. The core of the algorithm is based on perceptual time-frequency analysis and confidence measure based on distance distribution variance. The results show correct clustering in 100\% of cases for a real life dataset and surpass the performance of cross correlation while keeping lower system requirements.},
            pdf = {https://publications.idiap.ch/attachments/reports/2009/Korchagin_Idiap-RR-31-2009.pdf}
}