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ADBOT: Advertisement Recognition from TV and Radio Broadcasts

Aljon Rey Aniban Emmanuel Cagadas Anna Mae Yap. ADBOT: Advertisement Recognition from TV and Radio Broadcasts. Significance of Project.

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ADBOT: Advertisement Recognition from TV and Radio Broadcasts

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  1. Aljon Rey Aniban Emmanuel Cagadas Anna Mae Yap ADBOT: Advertisement Recognition from TV and Radio Broadcasts

  2. Significance of Project • In the Philippines, most advertising companies monitor commercials manually. If we could automate commercial monitoring like in other countries, it will be a great help to Philippine advertising companies not only because computers perform more accurately compared to humans but also this companies will save a lot if they use Filipino made technology rather than availing it abroad. • Less Human Labor + Less Cost + Improved accuracy

  3. Project Objectives • To determine the factors affecting accuracy • To determine the factors affecting real-time “implementability” • To determine how largely noise interruptions affect the performance of the system • To determine the maximum number of advertisements that can be stored in the database while not affecting its real time performance.

  4. Review of Related Literature • MediaGuide.com’sSeeSpotRun • Audio-Based Radio and TV Broadcast Monitoring , B. Oliveira, A. Crivellaro, and R. M. Ceasar Jr. • TV Advertisements Detection and Clustering based on Acoustic Information • A Highly Robust Audio Fingerprinting System , J. Haistma and T. Kalker

  5. Mediaguide.com • Mediaguide.com monitors music and advertising on over 2,700 college, non-commercial and commercial radio stations in 150 US markets; and over 3,500 internet stations in real-time, 24 hours per day, 7 days per week. • SeeSpotRun • monitors advertisements broadcasted across the radio and internet radio.

  6. Audio-Based Radio and TV Broadcast Monitoring • The IBOPE Media radio and TV broadcast monitoring has already developed a scalable real-time audio fingerprinting system. The system extracts temporal feature from audio using the Short-time Fast Fourier Transform (STFT). When given an input stream to analyze, the system matches it against the database and automatically recognizes instances of the previously registered samples within the input stream. The algorithm exploits the temporal evolution of the signal frequency spectrum in order to identify patterns and produce the final classification. The database is clusterized in order to provide an efficient and scalable search strategy.

  7. A Highly Robust Audio Fingerprinting System • This system presents a highly robust fingerprint extraction method by extracting 32bit sub-fingerprint every 11.8ms via looking at energy differences along the frequency and time axes. It uses very efficient fingerprint search strategy, which enables searching a large fingerprint database with only limited computing resources.

  8. Theoretical Framework • The project will be having 2 major components: • advertisement recognition module • user-interface module.

  9. TV Advertisements Detection and Clustering based on Acoustic Information • This system detects individual commercials within a broadcast and groups together all repetitions of the same commercial over time. Detection is done in three steps, incrementally refining an initial course detection. • Clustering is later done over all previously detected commercials to find out how many times each commercial appears. Clustering uses three algorithms: Standard Dynamic Time Warping, a simplified DTW(DTW mod) algorithm and a Generalized Cross-Correlation comparison.

  10. Theoretical Framework • The advertisement recognition model would be divided into: • segmentation (detecting start and end points), • windowing (dividing the audio file into narrow windows), • extracting the feature vector coefficients (fingerprints) using Short-Time Fourier Transform, • clustering of fingerprints in the database, • and efficiently searching and matching of the queried audio file.

  11. Database • MySQL database would be used to store fingerprints. Java would be used to code this system.

  12. Methodology • Populate the database with pre-computed fingerprints of the commercial to be monitored • Determine the start and end point of potential commercials in the audio input stream • Save the commercial to the memory for matching purposes • Segment the saved commercial into the appropriate length of frames

  13. Methodology • Then perform fingerprint extraction on each frames • Match the fingerprint to the database • Determine the number of occurrences of each commercial • Update the result

  14. Major Activities/Work Plans • Intensive research on signal processing • Record a collection of audio input from TV and radios with commercials • Database construction • Implementation of methodology

  15. Equipments Requirements • TV tuner/Radio Tuner • Java • MySql

  16. Thank you! THE END

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