Abstract
This paper proposes a new optimized audio-based fingerprinting technology for embedded applications. The target use case is related to TV content synchronization and its numerous applications for Social TV and second screen applications. The proposed technology can be used for automatically identifying the program being watched by capturing the sound of the TV set. It can also be used to know which program is being watched and to precisely estimate the timestamp of the currently broadcast moment with respect to the beginning of the program. This is very useful for second screen applications where notifications (e.g. quizzes, additional information, commercials) have to be sent to viewers with a perfect synchronization relatively to the broadcast TV program. The robustness of the proposed technique is first evaluated on a large music database and then by considering a realistic use case where this technology is embedded in a smartphone.
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References
Abduraman AE, Berrani S-A, Mrialdo B (2013) Audio/Visual recurrences and decision trees for unsupervised TV program structuring. In: VISAPP (1), pp 701–708
Balado F, Hurley NJ, McCarthy EP, Silvestre GC (2007) Performance analysis of robust audio hashing. IEEE Trans Inf Forensics Secur 2(2):254–266
Baluja S, Covell M (2006) Content fingerprinting using wavelets. In: Proceedings of the conference of Visual Media Production, IET
Bellettini C, Mazzini G (2010) A framework for robust audio fingerprinting. J Commun 5(5):409–424. doi:10.4304/jcm.5.5.409-424
Betser M, Collen P, Rault J-B (2007) Audio identification using sinusoidal modeling and application to jingle detection. In: Proceedings of the international conference on Music Information Retrieval, pp 139-142, Austrian Computer Society
Bisio I, Delfino A, Lavagetto F, Marchese M (2015) A television channel real-time detector using smartphones. IEEE Trans Mob Comput 14(1):14–27
Burges CJC, Plastina D, Platt JC, Renshaw E, Malva HS (2005) Using audio fingerprinting for duplicate detection and thumbnail generation. In: Proceedings of the IEEE international conference on Acoustic, Speech, Signal Processing. Philadelphia, PA, USA
Cano P (2007) Content-based audio search from fingerprinting to semantic audio retrieval. Ph.D. dissertation, University Pompeu Fabra, Barcelona, Spain
Cano P, Batlle E, Kalker T, Haitsma J (2005) A review of audio fingerprinting. J VLSI Sig Proc 41(3):271–284
Covell M, Baluja S (2009) LSH banding for large-scale retrieval with memory and recall constraints. In: Proceedings of the IEEE international conference on Acoustic, Speech, Signal Processing. Taipei, Taiwan
Duong NQK, Howson C, Legallais Y (2012) Fast second screen TV synchronization combining audio fingerprint technique and generalized cross correlation. In: Proceedings of the IEEE international conference on Consumer Electronics. Berlin, Germany
Haitsma J, Kalker T (2002) A highly robust audio fingerprinting system. In: Proceedings of the international conference on Music Information Retrieval, pp 107-115, Paris, France
Haitsma J, Kalker T (2003) A highly robust audio fingerprinting system with an efficient search strategy. J New Music Research 32(2)
Haitsma J, Kalker T, Oostveen J (2001) Robust audio hashing for content identification. In: Proceedings of the international workshop on Content-Based Multimedia Indexing, pp 117-125, Brescia, Italy
Liu Y, Cho K, Yun HS, Shin JW, Kim NS (2009) DCT based multiple hashing technique for robust audio fingerprinting. In: Proceedings of the IEEE international conference on Acoustic, Speech, Signal Processing. Taipei, Taiwan
Park M, Kim H-R, Yang SH (2006) Frequency-temporal filtering for a robust audio fingerprinting scheme in real-noise environments. ETRI J 28(4):509–512
Recommendation ITU-R BS.1657, Procedure for the performance testing of automated audio systems. Question ITU-R 8/6 (2003)
Wang A (2005) The Shazam music recognition service. In: Magazine Communications of the ACM - Music information retrieval
Xiao Q, Suzuki M, Kita K (2011) Fast Hamming space search for audio fingerprinting systems. In: ISMIR, pp 133-138. University of Miami
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Plapous, C., Berrani, SA., Besset, B. et al. A low-complexity audio fingerprinting technique for embedded applications. Multimed Tools Appl 77, 5929–5948 (2018). https://doi.org/10.1007/s11042-017-4505-4
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DOI: https://doi.org/10.1007/s11042-017-4505-4