A new television audience measurement framework using smart devices

Abstract

Television audience measurement is intended to collect information on the audiences watching a specific television program at a particular time. This information is crucial for television broadcasters and advertisers because they need to provide right television programs and commercials to right audiences to maximize their investments in broadcasting. For accurate measurements, a panel of representative audiences must be selected judiciously so that it accurately represents the entire target audience group. However, it is hard to secure a proper number of target audiences due to the expensive and cumbersome installations of measurement equipments. To resolve this issue in panel selection, we propose a novel television audience measurement framework using pervasive smart devices such as a smartphone. In the proposed framework, a short audio signal from a television set is recorded by a personal smart device and is sent to an audio matching server for the identification of the television program shown by the television set. For effective identification, we propose an accurate audio matching algorithm based on spectral coherence and efficient implementation techniques that exploit the inherent parallelism in the algorithm. To verify the plausibility of the framework and the effectiveness of the audio matching algorithm, we conduct experiments with diverse genres of television programs under various recording conditions.