Magister Legum - LLM / Global climate change is a serious, severe, and potentially irreversible problem. If no actions are taken to curb greenhouse gas emissions, global temperatures and sea levels will rise, wreaking havoc on earth, particularly in developing countries. The Stockholm Declaration of 1972 facilitated the first international consensus concerning the application of CBDR to international environmental problems. This was in reaction to the developing countries refusal to adhere to the same standards as the developed countries as they perceived this as a burden to their economic growth, which is unjust due to the developed countries historical culpability.

The subject of this is the design of a family autonomous vehicle, which is designed to transport four to five people. The work contains an analysis of current vehicles as well as concepts on the basis of which the direction in the field of mobility can be deduced. The aim of this thesis is to create an interior design that takes into account the expected social, technological and economic development.

Musical timbre transfer is the task of re-rendering the musical content of a given source using the rendering style of a target sound. The source keeps its musical content, e.g., pitch, microtiming, orchestration, and syncopation. I specifically focus on the task of transferring the style of percussive patterns extracted from polyphonic audio using a MelGAN-VC model [57] by training acoustic properties for each genre. Evaluating audio style transfer is challenging and typically requires user studies. An analytical methodology based on supervised and unsupervised learning including visualization for evaluating musical timbre transfer is proposed. The proposed methodology is used to evaluate the MelGAN-VC model for musical timbre transfer of drum tracks. The method uses audio features to analyze results of the timbre transfer based on classification probability from Random Forest classifier. And K-means algorithm can classify unlabeled instances using audio features and style-transformed results are visualized by t-SNE dimensionality reduction technique, which is helpful for interpreting relations between musical genres and comparing results from the Random Forest classifier. / Graduate