Although the identification of humans by fingerprints is a well–known technique in practice, the identification of an audio sample by means of a technique called audio fingerprinting is still under development. Audio fingerprinting can be used to identify different types of audio samples of which music and advertisements are the two most frequently encountered. Different audio fingerprinting techniques to identify audio samples appear seldom in the literature and direct comparisons of the techniques are not always available In this dissertation, the two audio fingerprinting techniques of Avery Wang and Haitsma and Kalker are compared in terms of accuracy, speed, versatility and scalability, with the goal of modifying the algorithms for optimal advertisement identification applications. To start the background of audio fingerprinting is summarised and different algorithms for audio fingerprinting are reviewed. Problems, issues to be addressed and research methodology are discussed. The research question is formulated as follows : “Can audio fingerprinting be applied successfully to advertisement monitoring, and if so, which existing audio fingerprinting algorithm is most suitable as a basis for a generic algorithm and how should the original algorithm be changed for this purpose?” The research question is followed by literature regarding the background of audio fingerprinting and different audio fingerprinting algorithms. Next, the importance of audio fingerprinting in the engineering field is motivated by the technical aspects related to audio fingerprinting. The technical aspects are not always necessary or part of the algorithm, but in most cases, the algorithms are pre–processed, filtered and downsampled. Other aspects include identifying unique features and storing them, on which each algorithm’s techniques differ. More detail on Haitsma and Kalker’s, Avery Wang’s and Microsoft’s RARE algorithms are then presented. Next, the desired interface for advertisement identification Graphical User Interface (GUI) is presented. Different solution architectures for advertisement identification are discussed. A design is presented and implemented which focuses on advertisement identification and helps with the validation process of the algorithm. The implementation is followed by the experimental setup and tests. Finally, the dissertation ends with results and comparisons, which verified and validated the algorithm and thus affirmed the first part of the research question. A short summary of the contribution made in the dissertation is given, followed by conclusions and recommendations for future work. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2012.

Although the identification of humans by fingerprints is a well–known technique in practice, the identification of an audio sample by means of a technique called audio fingerprinting is still under development. Audio fingerprinting can be used to identify different types of audio samples of which music and advertisements are the two most frequently encountered. Different audio fingerprinting techniques to identify audio samples appear seldom in the literature and direct comparisons of the techniques are not always available In this dissertation, the two audio fingerprinting techniques of Avery Wang and Haitsma and Kalker are compared in terms of accuracy, speed, versatility and scalability, with the goal of modifying the algorithms for optimal advertisement identification applications. To start the background of audio fingerprinting is summarised and different algorithms for audio fingerprinting are reviewed. Problems, issues to be addressed and research methodology are discussed. The research question is formulated as follows : “Can audio fingerprinting be applied successfully to advertisement monitoring, and if so, which existing audio fingerprinting algorithm is most suitable as a basis for a generic algorithm and how should the original algorithm be changed for this purpose?” The research question is followed by literature regarding the background of audio fingerprinting and different audio fingerprinting algorithms. Next, the importance of audio fingerprinting in the engineering field is motivated by the technical aspects related to audio fingerprinting. The technical aspects are not always necessary or part of the algorithm, but in most cases, the algorithms are pre–processed, filtered and downsampled. Other aspects include identifying unique features and storing them, on which each algorithm’s techniques differ. More detail on Haitsma and Kalker’s, Avery Wang’s and Microsoft’s RARE algorithms are then presented. Next, the desired interface for advertisement identification Graphical User Interface (GUI) is presented. Different solution architectures for advertisement identification are discussed. A design is presented and implemented which focuses on advertisement identification and helps with the validation process of the algorithm. The implementation is followed by the experimental setup and tests. Finally, the dissertation ends with results and comparisons, which verified and validated the algorithm and thus affirmed the first part of the research question. A short summary of the contribution made in the dissertation is given, followed by conclusions and recommendations for future work. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2012.

In recent years Content Based Information Retrieval (CBIR) has received a lot of research attention, starting with audio, followed by images and video. Video ngerprinting is a CBIR technique that creates a digital descriptor, also known as a ngerprint, for videos based on its content. These ngerprints are then saved to a database and used to detect unknown videos by comparing the unknown video's ngerprint to the ngerprints in the database to get a match. Many techniques have already been proposed with various levels of success, but most of the existing techniques focus mainly on robustness and neglect the speed of implementation. In this dissertation a novel video ngerprinting technique will be developed with the main focus on detecting advertisements in a television broadcast. Therefore the system must be able to process the incoming video stream in real-time and detect all the advertisements that are present. Even though the algorithm has to be fast, it still has to be robust enough to handle a moderate amount of distortions. These days video ngerprinting still holds many challenges as it involves characterizing videos, made up of sequences of images, e ectively. This means the algorithm must somehow imitate the inherent ability of humans to recognize a video almost instantly. The technique uses the content of the video to derive a ngerprint, thus the features used by the ngerprinting algorithm should be robust to distortions that don't a ect content according to humans. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013

In recent years Content Based Information Retrieval (CBIR) has received a lot of research attention, starting with audio, followed by images and video. Video ngerprinting is a CBIR technique that creates a digital descriptor, also known as a ngerprint, for videos based on its content. These ngerprints are then saved to a database and used to detect unknown videos by comparing the unknown video's ngerprint to the ngerprints in the database to get a match. Many techniques have already been proposed with various levels of success, but most of the existing techniques focus mainly on robustness and neglect the speed of implementation. In this dissertation a novel video ngerprinting technique will be developed with the main focus on detecting advertisements in a television broadcast. Therefore the system must be able to process the incoming video stream in real-time and detect all the advertisements that are present. Even though the algorithm has to be fast, it still has to be robust enough to handle a moderate amount of distortions. These days video ngerprinting still holds many challenges as it involves characterizing videos, made up of sequences of images, e ectively. This means the algorithm must somehow imitate the inherent ability of humans to recognize a video almost instantly. The technique uses the content of the video to derive a ngerprint, thus the features used by the ngerprinting algorithm should be robust to distortions that don't a ect content according to humans. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013