André Ebert

Stefan Langer, Liza Obermeier, André Ebert et al.

The world of linear radio broadcasting is characterized by a wide variety of stations and played content. That is why finding stations playing the preferred content is a tough task for a potential listener, especially due to the overwhelming number of offered choices. Here, recommender systems usually step in but existing content-based approaches rely on metadata and thus are constrained by the available data quality. Other approaches leverage user behavior data and thus do not exploit any domain-specific knowledge and are furthermore disadvantageous regarding privacy concerns. Therefore, we propose a new pipeline for the generation of audio-based radio station fingerprints relying on audio stream crawling and a Deep Autoencoder. We show that the proposed fingerprints are especially useful for characterizing radio stations by their audio content and thus are an excellent representation for meaningful and reliable radio station recommendations. Furthermore, the proposed modules are part of the HRADIO Communication Platform, which enables hybrid radio features to radio stations. It is released with a flexible open source license and enables especially small- and medium-sized businesses, to provide customized and high quality radio services to potential listeners.

Andre Ebert, Michael Till Beck, Andy Mattausch et al.

Smartphone applications designed to track human motion in combination with wearable sensors, e.g., during physical exercising, raised huge attention recently. Commonly, they provide quantitative services, such as personalized training instructions or the counting of distances. But qualitative monitoring and assessment is still missing, e.g., to detect malpositions, to prevent injuries, or to optimize training success. We address this issue by presenting a concept for qualitative as well as generic assessment of recurrent human motion by processing multi-dimensional, continuous time series tracked with motion sensors. Therefore, our segmentation procedure extracts individual events of specific length and we propose expressive features to accomplish a qualitative motion assessment by supervised classification. We verified our approach within a comprehensive study encompassing 27 athletes undertaking different body weight exercises. We are able to recognize six different exercise types with a success rate of 100% and to assess them qualitatively with an average success rate of 99.3%.