Robust Radio Broadcast Monitoring Using a Multi-Band Spectral Entropy Signature

Authors:
Antonio Camarena-Ibarrola;Edgar Chávez;Eric Sadit Tellez
Affiliations:
Universidad Michoacana,;Universidad Michoacana, and CICESE,;Universidad Michoacana,
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

Citing 3
Cited 4

Audio watermarking for monitoring and copy protection

MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Audio-based radio and TV broadcast monitoring

WebMedia '05 Proceedings of the 11th Brazilian Symposium on Multimedia and the web
On musical performances identification, entropy and string matching

MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence

Real time tracking of musical performances

MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Environmental sound recognition by measuring significant changes in the spectral entropy

MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Activity recognition using a spectral entropy signature

Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Automatic monitoring the content of audio broadcasted by internet radio stations

MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Monitoring media broadcast content has deserved a lot of attention lately from both academy and industry due to the technical challenge involved and its economic importance (e.g. in advertising). The problem pose a unique challenge from the pattern recognition point of view because a very high recognition rate is needed under non ideal conditions. The problem consist in comparing a small audio sequence (the commercial ad) with a large audio stream (the broadcast) searching for matches. In this paper we present a solution with the Multi-Band Spectral Entropy Signature (MBSES) which is very robust to degradations commonly found on amplitude modulated (AM) radio. Using the MBSES we obtained perfect recall (all audio ads occurrences were accurately found with no false positives) in 95 hours of audio from five different am radio broadcasts. Our system is able to scan one hour of audio in 40 seconds if the audio is already fingerprinted (e.g. with a separated slave computer), and it totaled five minutes per hour including the fingerprint extraction using a single core off the shelf desktop computer with no parallelization.