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Multimedia Tools and Applications

, Volume 73, Issue 3, pp 1757–1776 | Cite as

A new television audience measurement framework using smart devices

  • Chungsoo Lim
  • Jae-Hoon Choi
  • Sang Won Nam
  • Joon-Hyuk Chang
Article

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.

Keywords

Television audience measurement Audio matching Spectral coherence Smartphone 

References

  1. 1.
    Allamanche E, Herre J, Hellmuch O, Bernhard B, Cremer M (2001) AudioID: towards content-based identification of audio material. In: Proceedings of 2001 AES international convention. Amsterdam, Netherland, pp 1–11Google Scholar
  2. 2.
    Blum T, Keislar D, Wheaton J, Wold E (1999) Method and article of manufacture for content-based analysis, storage, retrieval and segmentation of audio information. U.S. Patent 5,918,223Google Scholar
  3. 3.
    Butte AJ, Bao L, Reis BY, Watkins TW, Kohane IS (2001) Comparing the similarity of time-series gene expression using signal processing metrics. J Biomed Inform 34(6):396–405CrossRefGoogle Scholar
  4. 4.
    Cano P (2002) A review of algorithms for audio fingerprinting. In: Proceedings of 2002 IEEE workshop on multimedia signal processing (MMSP). St. Thomas, USA, pp 169–173Google Scholar
  5. 5.
    Cano P, Batlle E, Mayer H, Neuschmied H (2002) Robust sound modeling for song detection in broadcast audio. In: Proceedings of 2002 AES international convention. Munich, Germany, pp 1–7Google Scholar
  6. 6.
    Cano P, Batlle E, Gomez E, Gomes L, Bonnet M (2005) Audio fingerprinting: concept and applications. In: Halgamuge SK, Wang L (eds) Computational intelligence for modelling and prediction, vol 2. Springer, Berlin Heidelberg, pp 233–245Google Scholar
  7. 7.
    Cockshott P, Renfrew K (2010) SIMD programming manual for Linux and Windows. SpringerGoogle Scholar
  8. 8.
    Cohen EAK, Walden AT (2010) A statistical study of temporally smoothed wavelet coherence. IEEE Trans Signal Process 58(6):2964–2973CrossRefMathSciNetGoogle Scholar
  9. 9.
    Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms. The MIT Press, pp 28–33Google Scholar
  10. 10.
    Culler DE, Singh JP, Gupta A (1999) Parallel computer architecture: a hardware/software approach. Morgan Kaufmann, pp 124–125Google Scholar
  11. 11.
    Deng J, Wan W, Swaminathan R, Yu X, Pan X (2011) An audio fingerprinting system based on spectral energy structure. In: Proceedings of 2011 IEEE international conference on communication technology. Jinan, China, pp 1103–1106Google Scholar
  12. 12.
    Fourie PJ (2010) Media Studies: media content and media audiences. Juta Academic, Lansdowne, pp 515–540Google Scholar
  13. 13.
    Fragoulis D, Rousopoulos G, Panagopoulos T, Alexiou C, Papaodysseus C (2001) On the automated recognition of seriously distorted musical recordings. IEEE Trans Signal Process 49(4):898–908CrossRefGoogle Scholar
  14. 14.
    Haitsma J, Kalker T (2002) A highly robust audio fingerprinting system. In: Proceedings of 2002 international society for music information retrieval. Paris, France, pp 144–148Google Scholar
  15. 15.
    Herre A, Allamanche E, Hellmuth O (2001) Robust matching of audio signals using spectral flatness features. In: Proceedings of 2001 IEEE workshop on the applications of signal processing to audio and acoustics. New Platz, USA, pp 127–130Google Scholar
  16. 16.
    Jeong S, Lee S, Hahn M (2008) Dual microphone-based speech enhancement by spectral classification and Wiener filtering. Electron Lett 44(3):253–254CrossRefGoogle Scholar
  17. 17.
    Kastner T, Allamanche E, Herre J, Hellmuth O, Cremer M, Grossmann H (2002) MPEG-7 scalable robust audio fingerprinting. In: Proceedings of 2002 AES international convention. Munich, Germany, pp 8–14Google Scholar
  18. 18.
    Kimura A, Kashino K, Kurozumi T, Murase H (2001) Very quick audio searching: introducing global pruning to the time-series active search. In: Proceedings of 2001 international conference on computational intelligence and multimedia applications. Salt Lake City, USA, pp 1429–1432Google Scholar
  19. 19.
    Kus R, Kaminski M, Blinowska KJ (2004) Determination of EEG activity propagation: pair-wise versus multichannel estimate. IEEE Trans Biomed Eng 51(9):1501–1510CrossRefGoogle Scholar
  20. 20.
    Mytton G (1999) Handbook on radio and television audience research. Stationary Office BooksGoogle Scholar
  21. 21.
    Nichols B, Buttlar D, Farrell JP (1996) PThreads Programming: a POSIX standard for better multiprocessing. O’Reilly MediaGoogle Scholar
  22. 22.
    Rafailidis D, Nanopoulos A, Manolopoulos Y (2011) Nonlinear dimensionality reduction for efficient and effective audio similarity searching. Multimed Tools Appl 51:881–895CrossRefGoogle Scholar
  23. 23.
    Rahmani M, Akbari A, Ayad B (2009) An iterative noise cros-PSD estimation for two-microphone speech enhancement. Appl Acoust 70(3):512–521CrossRefGoogle Scholar
  24. 24.
    Sukittanon S, Atlas L (2002) Modulation frequency features for audio fingerprinting. In: Proceedings of 2002 international conference on acoustics, speech, and signal processing. Orland, USA, pp 1173–1176Google Scholar
  25. 25.
    Thomas WL (1992) Television audience research technology, today’s systems and tomorrow’s challenges. IEEE T Consum Electr 38(3):XXXIX–XXLIIGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Chungsoo Lim
    • 1
  • Jae-Hoon Choi
    • 1
  • Sang Won Nam
    • 1
  • Joon-Hyuk Chang
    • 1
  1. 1.Hanyang UniversitySeongdongRepublic of Korea

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