Thesis (Ph. D.)--University of Chicago, Department of Chemistry, 2002. / Includes bibliographical references. Also available on the Internet.

Polybrominated flame retardants (BFRs), organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) were analysed in eggs of various wild bird species from industrialised areas in South Africa. Eggs were collected during the 2008 – 2009 breeding season, homogenised and sent to the Norwegian School of Veterinary Science (NVH) for gas chromatography-mass spectrometry (GC-MS) analysis. The concentration, contamination profile, and risk assessment were conducted for each pollutant class, while effects of species-specific variation, feeding guild, and feeding habitat were investigated. Levels of BFRs ranged between 2.6 – 44 ng g-1 wet mass (wm). The predominant congeners were BDE-153, -154, - 183 and -47. Results indicated species, in close contact to humans, had higher levels of BFRs, even at lower trophic levels. Therefore, diet was not the primary route of exposure. High concentrations and the occurrence of nona-PBDE congeners and HBCD indicated exposure to current use BFRs. There were measurable levels of OCPs and PCBs in all eggs analysed. Median OCP concentration ranged from 4.2 – 623 ng g-1 wm. DDE was the predominant compound in all species with the exception of the Crowned Lapwing (Vanellus coronatus) where chlordanes were predominant. This may indicate a species-specific attribute in the metabolic efficiency or diet of the genus, since these findings have been reported elsewhere in literature. Congener profiles indicated historic sources of lindane and DDT, while low levels of p,p’-DDT in al species indicate long-range or atmospheric transport. Even though levels of p,p’-DDE were approaching toxicological thresholds, no eggshell thinning was evident. Concentrations of OCPs and PCBs showed an increase with increasing tophic level. PCB concentrations ranged between 0.9 – 296.4 ng g-1 wm. When studying the metabolic potential of PCBs, metabolic groups showed good agreement with the biodegradability of the individual congeners. Phenobarbital-type (PB-type) inducer PCBs were prevalent, indicating the predominance of less toxic PCB congeners. However, non-ortho PCBs were not analysed. These congeners aslo could impact on the toxic potential of PCBs in wild bird eggs. Principle component analysis (PCA) indicated that variances within datasets could be attributed to congener profiles within species as they were affected by exposure, diet, position in the food web, and association with human activities. Although the individual groups of organohalogens were below no observed effect levels (NOELs), negative effects could occur through interactions of various compounds with each other, as well as the unique exposure profiles of South African bird populations. To assess the dietary exposure of low-income human populations living close to large industries, the occurrence of organohalogens was investigated in backyard chicken eggs. Levels of dioxins in these eggs were above the European Union (EU) recommended limits, whereas BFRs and OCPs levels were below levels of concern. Nevertheless, areas where DDT is actively applied to dwellings for malaria control should be urgently investigated. The presence of measureable levels of all the compounds considered, indicate an environment seriously impacted by anthropogenic activity that in the long term could negatively affect both the environment and human health, if it has not already done so. / Thesis (Ph.D. (Environmental Science))--North-West University, Potchefstroom Campus, 2011.

Polybrominated flame retardants (BFRs), organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) were analysed in eggs of various wild bird species from industrialised areas in South Africa. Eggs were collected during the 2008 – 2009 breeding season, homogenised and sent to the Norwegian School of Veterinary Science (NVH) for gas chromatography-mass spectrometry (GC-MS) analysis. The concentration, contamination profile, and risk assessment were conducted for each pollutant class, while effects of species-specific variation, feeding guild, and feeding habitat were investigated. Levels of BFRs ranged between 2.6 – 44 ng g-1 wet mass (wm). The predominant congeners were BDE-153, -154, - 183 and -47. Results indicated species, in close contact to humans, had higher levels of BFRs, even at lower trophic levels. Therefore, diet was not the primary route of exposure. High concentrations and the occurrence of nona-PBDE congeners and HBCD indicated exposure to current use BFRs. There were measurable levels of OCPs and PCBs in all eggs analysed. Median OCP concentration ranged from 4.2 – 623 ng g-1 wm. DDE was the predominant compound in all species with the exception of the Crowned Lapwing (Vanellus coronatus) where chlordanes were predominant. This may indicate a species-specific attribute in the metabolic efficiency or diet of the genus, since these findings have been reported elsewhere in literature. Congener profiles indicated historic sources of lindane and DDT, while low levels of p,p’-DDT in al species indicate long-range or atmospheric transport. Even though levels of p,p’-DDE were approaching toxicological thresholds, no eggshell thinning was evident. Concentrations of OCPs and PCBs showed an increase with increasing tophic level. PCB concentrations ranged between 0.9 – 296.4 ng g-1 wm. When studying the metabolic potential of PCBs, metabolic groups showed good agreement with the biodegradability of the individual congeners. Phenobarbital-type (PB-type) inducer PCBs were prevalent, indicating the predominance of less toxic PCB congeners. However, non-ortho PCBs were not analysed. These congeners aslo could impact on the toxic potential of PCBs in wild bird eggs. Principle component analysis (PCA) indicated that variances within datasets could be attributed to congener profiles within species as they were affected by exposure, diet, position in the food web, and association with human activities. Although the individual groups of organohalogens were below no observed effect levels (NOELs), negative effects could occur through interactions of various compounds with each other, as well as the unique exposure profiles of South African bird populations. To assess the dietary exposure of low-income human populations living close to large industries, the occurrence of organohalogens was investigated in backyard chicken eggs. Levels of dioxins in these eggs were above the European Union (EU) recommended limits, whereas BFRs and OCPs levels were below levels of concern. Nevertheless, areas where DDT is actively applied to dwellings for malaria control should be urgently investigated. The presence of measureable levels of all the compounds considered, indicate an environment seriously impacted by anthropogenic activity that in the long term could negatively affect both the environment and human health, if it has not already done so. / Thesis (Ph.D. (Environmental Science))--North-West University, Potchefstroom Campus, 2011.

Aquesta tesi versa sobre la classificació automàtica de gèneres musicals, basada en l'anàlisi del contingut del senyal d'àudio, plantejant-ne els problemes i proposant solucions. Es proposa un estudi de la classificació de gèneres musicals des del punt de vista computacional però inspirat en teories dels camps de la musicologia i de la percepció. D'aquesta manera, els experiments presentats combinen diferents elements que influeixen en l'encert o fracàs de la classificació, com ara els descriptors d'àudio, les tècniques d'aprenentatge, etc. L'objectiu és avaluar i comparar els resultats obtinguts d'aquests experiments per tal d'explicar els límits d'encert dels algorismes actuals, i proposar noves estratègies per tal de superar-los. A més a més, partint del processat de la informació d'àudio, s'inclouen aspectes musicals i culturals referents al gènere que tradicionalment no han estat tinguts en compte en els estudis existents. En aquest context, es proposa l'estudi de diferents famílies de descriptors d'àudio referents al timbre, ritme, tonalitat o altres aspectes de la música. Alguns d'aquests descriptors són proposats pel propi autor mentre que d'altres ja són perfectament coneguts. D'altra banda, també es comparen les tècniques d'aprenentatge artificial que s'usen tradicionalment en aquest camp i s'analitza el seu comportament davant el nostre problema de classificació. També es presenta una discussió sobre la seva capacitat per representar els diferents models de classificació proposats en el camp de la percepció. Els resultats de la classificació es comparen amb un seguit de tests i enquestes realitzades sobre un conjunt d'individus. Com a resultat d'aquesta comparativa es proposa una arquitectura específica de classificadors que també està raonada i explicada en detall. Finalment, es fa un especial èmfasi en comparar resultats dels classificadors automàtics en diferents escenaris que pressuposen la barreja de bases de dades, la comparació entre bases de dades grans i petites, etc. A títol de conclusió, es mostra com l'arquitectura de classificació proposada, justificada pels resultats dels diferents anàlisis, pot trencar el límit actual en tasques de classificació automàtica de gèneres musicals. De manera condensada, es pot dir que aquesta tesi contribueix al camp de la classificació de gèneres musicals en els següents aspectes: a) Proporciona una revisió multidisciplinar delsgèneres musicals i la seva classificació; b)Presenta una avaluació qualitativa i quantitativa de les famílies de descriptors d'àudio davant el problema de la classificació de gèneres; c) Avalua els pros i contres de les diferents tècniques d'aprenentatge artificial davant el gènere; d) Proposa una arquitectura nova de classificador d'acord amb una visió interdisciplinar dels gèneres musicals; e) Analitza el comportament de l'arquitecturaproposada davant d'entorns molt diversos en el que es podria implementar el classificador. / Esta tesis estudia la clasificación automática degéneros musicales, basada en el análisis delcontenido de la señal de audio, planteando sus problemas y proponiendo soluciones. Sepropone un estudio de la clasificación de los géneros musicales desde el punto de vista computacional, pero inspirado en teorías de los campos de la musicología y la percepción. De este modo, los experimentos persentados combinan distintos elementos que influyen en el acierto o fracaso de la clasificación, como por ejemplo los descriptores de audio, las técnicas de aprondiza je, etc. El objetivo es comparar y evaluar los resultados obtenidos de estos experimentos para explicar los límites de las tasas de acierto de los algorismos actuales, y proponer nuevas estrategias para superarlos. Además, partiendo del procesado de la información de Audio, se han incluido aspectos musicales y culturales al género que tradicionalmente no han sido tomados en cuenta en los estudios existentes. En este contexto, se propone el estudio de distintas famílias de descriptores de audio referentes al timbre, al ritmo, a la tonalidad o a otros aspectos de la música. Algunos de los descriptores son propuestos por el mismo autor, mientras que otros son perfectamente conocidos. Por otra parte, también se comparan las técnicas de aprendiza je artificial que se usan tradicionalmente, y analizamos su comportamiento en frente de nuestro problema de clasificación. Tambien planteamos una discusión sobre su capacidad para representar los diferentes modelos de clasificación propuestos en el campo de la percepción. Estos resultados de la clasificación se comparan con los resultados de unos tests y encuestas realizados sobre un conjunto de individuos. Como resultado de esta comparativa se propone una arquitectura específica de clasificadores que tambien está razonada y detallada en el cuerpo de la tesis. Finalmente, se hace un émfasis especial en comparar los resultados de los clasificadores automáticos en distintos escenarios que assumen la mezcla de bases de datos, algunas muy grandes y otras muy pequeñas, etc. Como conclusión, mostraremos como la arquitectura de clasificación propuesta permite romper el límite actual en el ámbito de la classificación automática de géneros musicales.De forma condensada, se puede decir que esta tesis contribuye en el campo de la clasificación de los géneros musicales el los siguientes aspectos: a) Proporciona una revisión multidisciplinar de los géneros musicales y su clasificación; b) Presenta una evaluación cualitativa y cuantitativa de las famílias de descriptores de audio para la clasificación de géneros musicales; c) Evalua los pros y contras de las distintas técnicas de aprendiza je artificial delante del género; d) Propone una arquitectura nueva del clasificador de acuerdo con una visión interdisciplinar de los géneros musicales; e) Analiza el comportamiento de la arquitectura propuesta delante de entornos muy diversos en los que se podria implementar el clasificador. / This dissertation presents, discusses, and sheds some light on the problems that appear when computers try to automatically classify musical genres from audio signals. In particular, a method is proposed for the automatic music genre classification by using a computational approach that is inspired in music cognition and musicology in addition to Music Information Retrieval techniques. In this context, we design a set of experiments by combining the different elements that may affect the accuracy in the classification (audio descriptors, machine learning algorithms, etc.). We evaluate, compare and analyze the obtained results in order to explain the existing glass-ceiling in genre classification, and propose new strategies to overcome it. Moreover, starting from the polyphonic audio content processing we include musical and cultural aspects of musical genre that have usually been neglected in the current state of the art approaches. This work studies different families of audio descriptors related to timbre, rhythm, tonality and other facets of music, which have not been frequently addressed in the literature. Some of these descriptors are proposed by the author and others come from previous existing studies. We also compare machine learning techniques commonly used for classification and analyze how they can deal with the genre classification problem. We also present a discussion on their ability to represent the different classification models proposed in cognitive science. Moreover, the classification results using the machine learning techniques are contrasted with the results of some listening experiments proposed. This comparison drive us to think of a specific architecture of classifiers that will be justified and described in detail. It is also one of the objectives of this dissertation to compare results under different data configurations, that is, using different datasets, mixing them and reproducing some real scenarios in which genre classifiers could be used (huge datasets). As a conclusion, we discuss how the classification architecture here proposed can break the existing glass-ceiling effect in automatic genre classification. To sum up, this dissertation contributes to the field of automatic genre classification: a) It provides a multidisciplinary review of musical genres and its classification; b) It provides a qualitative and quantitative evaluation of families of audio descriptors used for automatic classification; c) It evaluates different machine learning techniques and their pros and cons in the context of genre classification; d) It proposes a new architecture of classifiers after analyzing music genre classification from different disciplines; e) It analyzes the behavior of this proposed architecture in different environments consisting of huge or mixed datasets.