COMMERCIAL DETECTION BY MINING MAXIMAL REPEATED SEQUENCE IN AUDIO STREAM
Jiansong Chen, Teng Li, Lei Zhu, Peng Ding, Bo XuAbstract
Efficient detection of commercial is an important topic for many applications such as commercial monitoring, market investigation. This paper reports an unsupervised technique of discovering commercial by mining repeated sequence in audio stream. Compared with previous work, we focus on solving practical problems by introducing three principles of commercial: repetition principle, independence principle and equivalence principle. Based on these principles, we detect the commercials by first mining maximal repeated sequences (MRS) and then post-processing the MRS pairs based on independence principle and equivalence principle for final result. In addition, a coarse-to-fine scheme is adopted in the acoustic matching stage to save computational cost. Extensive experiments both on simulated data and real broadcast data demonstrate the effectiveness of our method.
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