The GTZAN dataset, a collection of 1000 songsspanning 10 genres, proposed by Tzanetakis hasbeen around for 20 years. In this time hundredsof researches and applications have included thisdatabase. However, there seem to be some seri-ous limitations to this dataset. There are dupli-cates, mislabellings, low audio recordings and nar-row representations of genres. This paper aimsto research the effects of both audio quality andthe content of this dataset on genre classification.A Support Vector Machine (SVM) has been usedto retrain and compare different versions of thedataset. Two experiments have been proposed inthe paper. In the first experiment, a comparison be-tween a lossless dataset of high audio quality andan mp3 version of that same dataset of a loweraudio quality have been investigated. The lowerquality dataset performed worse on the SVM clas-sifier of this size. The second experiment pro-posed a new metal dataset, based on a wider andmore balanced range of metal sub-genres. Thismetal dataset has replaced the original metal partof the GTZAN dataset. Some retrainings done thisway had a higher accuracy than the original, givingconfidence that representing a well-balanced genremight improve classification performance. Finally,it has been found that the original GTZAN classi-fier is inaccurate on audio samples outside of itsdataset, where the new retrainings done on losslessdatasets without much preprocessing seem to per-form substantially better. This last finding has notbeen verified systematically and asks for more ver-ification.

This thesis investigates the socio-technical factors influencing the use of Agile methods within project teams in local government organizations in the Netherlands, aiming to develop a practical framework to enhance Agile practices. The primary research question is: How can project teams in local governments stimulate the use of Agile methods? To answer this, the study addresses three sub-questions: (1) What team archetypes represent the typical use of Agile methods in local governments? (2) WWhat barriers and drivers towards the use of Agile methods are experienced by the teams? (3) How do different types of (Agile) team management impact the perceived performance by team members? Data was collected through qualitative methods, including interviews with team representatives of 15 teams across three local government organizations, and surveys with team members from one of these government organizations. The study categorizes teams into archetypes based on their Agile methodologies, reason to change, experience levels, and management support. Key barriers include lack of management support, lack of focus, lack of knowledge, unclear roles, lack of alignment and organizational resistance to change. Important drivers for Agile adoption include Agile mindset, Agile coaching, dedicated key roles, dedicated management and freedom to experiment and structure. No significant difference in perceived performance was observed between team archetypes, indicating that perceived performance may be influenced by a broader range of factors beyond the scope of this study. The theoretical contribution extends the understanding of Agile adoption in public sector contexts, while the practical framework provides actionable steps for local government teams to navigate barriers and leverage drivers. Future research should broaden the scope to include diverse local government contexts, integrate quantitative data, and explore the long-term impacts of Agile practices and the application of the framework in a government, providing valuable data to inform ongoing Agile transformations in government settings.