Auto-tagging systems can enrich music audio by providing contextual information in the form of tag predictions. Such context is valuable to solve problems within the MIR field. The majority of re- cent auto-tagging research, however, only considers a fraction of tags from the full set of available annotations in the original datasets. Because of this restriction, potential relationships between tags remain unconsidered and tagging may be less rich. These relationships suggest alternative ways to establish an auto-tagging system. For instance, a few accurate annotations from experts can improve the richness and quality of the auto-tagging system by providing explicit context in addition to audio content features. In this work, we propose an adaptation to the auto-tagging task, semi auto-tagging, to demonstrate such potential. In our framework, tags are allowed as contextual input to the tag pre- diction system in addition to audio content information. The system then suggests additional relevant tags. We implement two models that fit within the framework: content-aware matrix factorization and graph convolutional networks. To see whether we can improve upon a traditional auto-tagger, we compare these models with a multilayer perceptron as a baseline. Experimental results show that semi auto-tagger models can predict relevant tags both in the absence and presence of an audio content feature, and can predict tags for previously unseen songs similarly to an audio content auto-tagger. Based on a tag embedding comparison, we find that semi auto-tagger models can better learn implicit relationships between tags with a similar text string representation when compared to the baseline.

A start-up creates videos which users can watch to experience their running or cycling activity all over again. Currently, the company depends on external data sources to generate a video. To be less dependent on these sources the company wants to create their own tracking solution. This solution has to fit in their existing smartphone application available for iOS and Android. The company wants to remain flexible, therefore the tracking application has to be developed in such a way that it can also be used in other products the company might develop in the future. As a goal, the data has to result in visually pleasing videos for a large user base.

Based on an experimental app developed during the research phase, raw smartphone GPS data was found to be unsuitable for video rendering. To improve this data, a Kalman Filter is used, in combination with a smoothing algorithm. The system has been designed to allow code sharing between iOS and Android where possible. The system has been implemented in Objective-C, Java, and TypeScript. Separating the system in three blocks enables code reuse which improves maintainability of the system. The filter has been integrated as shared code in the TypeScript implementation, which allows filtering to happen on the device. The user of the React Native Module developed has freedom to retrieve the unprocessed and processed data.

The system has been tested by means of unit tests in all three programming languages used. Tests have been executed using a continuous integration server, testing each pull request against the current code base to ensure quality. Part of the testing phase includes the React Native Module to be implemented in the client's smartphone application to demonstrate its use. The application has been sent to a number of test participants to collect data from different routes and activities. The project can be seen as a success since all important requirements have been successfully implemented.