@inproceedings{0188471a08654db5b952adb2878d94eb,
title = "Model-based decoding metrics for content identification",
abstract = "In this paper, decoding metrics are designed for statistical fingerprint-based content identification. A fairly general class of structured codes is considered, and a statistical model for the resulting fingerprints and their degraded versions (following miscellaneous content distortions) is proposed and validated. The Maximum-Likelihood fingerprint decoder derived from this model is shown to considerably improve upon previous decoders based on the Hamming metric. A GLRT test is also proposed and evaluated to deal with unknown distortion channels.",
keywords = "Content identification, audio, fingerprinting, hashing, maximum likelihood decoding, video",
author = "Rohit Naini and Pierre Moulin",
year = "2012",
doi = "10.1109/ICASSP.2012.6288257",
language = "English (US)",
isbn = "9781467300469",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "1829--1832",
booktitle = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings",
note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",
}