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.

La recerca presentada en aquesta dissertació glossa sobre transformacions de tempo de gravacions monofòniques de saxo jazz preservant l'expressivitat musical. Es una contribució al processament d'audio basat en el contingut, un camp de recerca que ha emergit recentment com a resposta a la necessitat creixent de gestionar intel·ligentment la creixent quantitat d'informació digital multimedia disponible actualment. S'ha investigat com una execució musical, tocada a un tempo concret, es pot reproduir automàticament a un altre tempo mantenint l'expressivitat. Aquest problema no es pot reduir a aplicar una transformació uniforme a totes les notes de la melodia, operació que degradaria la qualitat de l'execució. Proposem un sistema de raonament basat en casos per a transformacions de tempo preservant l'expressivitat. La validació del sistema mostra un comportament superior a la transformació uniforme. A m'es, s'han fet contribucions a l'anàlisi de gravacions expressives, CBR, recuperació de melodies i metodologires d'evaluació de models d'expressivitat. / The research presented in this dissertation focuses on expressivity-aware tempo transformations of monophonic audio recordings of saxophone jazz performances. It is a contribution to content-based audio processing, a field of technology that has recently emerged as an answer to the increased need to deal intelligently with the evergrowing amount of digital multimedia information available nowadays. We have investigated the problem of how a musical performance played at a particular tempo can be rendered automatically at another tempo, while preserving naturally sounding expressivity. This problem cannot be reduced to just applying a uniform transformation to all notes of the melody, since it often degrades the musical quality of the performance. We present a case-based reasoning system for expressivity aware tempo transformations. A validation of the system showed superior results compared to uniform transformation. Furthermore, contributions have been made to expressive performance analysis, CBR, melody retrieval, and evaluation methodologies of expressive models.

Music mood recognition by machine continues to attract attention from both academia and industry. This thesis explores the hypothesis that the music emotion problem is circular, and is a primary step in determining the efficacy of circular regression as a machine learning method for automatic music mood recognition. This hypothesis is tested through experiments conducted using instances of the two commonly accepted models of affect used in machine learning (categorical and two-dimensional), as well as on an original circular model proposed by the author. Polygonal approximations of circular regression are proposed as a practical way to investigate whether the circularity of the annotations can be exploited. An original dataset assembled and annotated for the models is also presented. Next, the architecture and implementation choices of all three models are given, with an emphasis on the new polygonal approximations of circular regression. Experiments with different polygons demonstrate consistent and in some cases significant improvements over the categorical model on a dataset containing ambiguous extracts (ones for which the human annotators did not fully agree upon). Through a comprehensive analysis of the results, errors and inconsistencies observed, evidence is provided that mood recognition can be improved if approached as a circular problem. Finally, a proposed multi-tagging strategy based on the circular predictions is put forward as a pragmatic method to automatically annotate music based on the circular model. / Graduate / 0984 / 0800 / 0413 / zazz101@hotmail.com

Aquesta tesi tracta de cercadors d'audio basats en contingut. Específicament, tracta de desenvolupar tecnologies que permetin fer més estret l'interval semàntic o --semantic gap' que, a avui dia, limita l'ús massiu de motors de cerca basats en contingut. Els motors de cerca d'àudio fan servir metadades, en la gran majoria generada per editors, per a gestionar col.leccions d'àudio. Tot i ser una tasca àrdua i procliu a errors, l'anotació manual és la pràctica més habitual. Els mètodes basats en contingut àudio, és a dir, aquells algorismes que extreuen automàticament etiquetes descriptives de fitxers d'àudio, no són generalment suficientment madurs per a permetre una interacció semàntica. En la gran majoria, els mètodes basats en contingut treballen amb descriptors de baix nivell, mentre que els descriptors d'alt nivell estan més enllà de les possibilitats actuals. En la tesi explorem mètodes, que considerem pas previs per a atacar l'interval semàntic. / This dissertation is about audio content-based search. Specifically, it is on developing technologies for bridging the semantic gap that currently prevents wide-deployment of audio content-based search engines.Audio search engines rely on metadata, mostly human generated, to manage collections of audio assets.Even though time-consuming and error-prone, human labeling is a common practice.Audio content-based methods, algorithms that automatically extract description from audio files, are generally not mature enough to provide a user friendly representation for interacting with audio content. Mostly, content-based methods are based on low-level descriptions, while high-level or semantic descriptions are beyond current capabilities. In this thesis we explore technologies that can help close the semantic gap.