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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

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

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Page 1: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

ADBOT: ADVERTISEMENT RECOGNITION FROM TV

AND RADIO BROADCASTS

Aljon Rey AnibanEmmanuel Cagadas

Anna Mae Yap

Page 2: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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

Page 3: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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.

Page 4: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

Review of Related Literature MediaGuide.com’s SeeSpotRun 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

Page 5: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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.

Page 6: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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.

Page 7: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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.

Page 8: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

Theoretical Framework The project will be having 2 major

components: advertisement recognition moduleuser-interface module.

Page 9: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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.

Page 10: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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.

Page 11: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

Database MySQL database would be used to store

fingerprints. Java would be used to code this system.

Page 12: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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

Page 13: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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

Page 14: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

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

Page 15: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

Equipments Requirements TV tuner/Radio Tuner Java MySql

Page 16: ADBOT:  Advertisement  Recognition from TV and Radio Broadcasts

THE ENDThank you!