9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

Advertisement Detection in French Broadcast News Using Acoustic Repetition and Gaussian Mixture Models

Vishwa Gupta, Gilles Boulianne, Patrick Kenny, Pierre Dumouchel

CRIM, Canada

In this paper, we detect advertisements in French broadcast news by locating both repeated and non-repeated ads. The non-repeated ads are located by using Gaussian mixture models (GMMs) to discriminate between program and ad segments. The repeated ad detection stage first uses features generated by a symmetric KL2 metric to locate repeated 5-sec audio segments. These repeated segments are then verified and extended through a detailed matching algorithm that uses cepstral features. The proposed repeated advertisement detection algorithms detect repeated audio reliably, resulting in 33.2% advertisement detection error rate (AER). The 26.0% missed ads are due to ads not being repeated, while the 7.2% false alarms are due to short repeated segments in the program. Using GMMs to classify repeated segments as program or ad reduces the AER to 30.1%. To locate non-repeated ads in program segments, we divide the audio between these repeated ads into short segments, and classify each segment as a program or an ad using these GMMs. This reduces the AER from 30.1% to 13.0%. We improve the segment boundaries between programs and ads by Viterbi alignment. This re-alignment reduces the AER from 13.0% to 10.6% (96.7% recall and 93.0% precision). Overall, we detect 87% of the non-repeated ads.

Full Paper

Bibliographic reference.  Gupta, Vishwa / Boulianne, Gilles / Kenny, Patrick / Dumouchel, Pierre (2008): "Advertisement detection in French broadcast news using acoustic repetition and Gaussian mixture models", In INTERSPEECH-2008, 2538-2541.