Verkkoradio, musiikki ja radio - katsaus verkkoradioihin osana suomalaisten musiikin kuuntelua

This thesis examines Internet-radios and other web-based music services, and different ways these services are used in music listening in Finland. The research material was gathered in eight interviews that took place between spring 2005 and spring 2006 in southern Finland. The analysis distinguishes between five main types of Internet-radios: a) simulcasting, b) webcasting, c) podcasting, d) web-based sound archives, e) interactive music services. As a medium for music listening these combine aspects of computers and traditional radio. The role of Internet-radios in everyday life as well as different types of listening motivation are examined in the light of earlier research on taste, music listening and radio listening.

Semi-supervised learning of WLAN radio maps

In this thesis a manifold learning method is applied to the problem of WLAN positioning and automatic radio map creation. Due to the nature of WLAN signal strength measurements, a signal map created from raw measurements results in non-linear distance relations between measurement points. These signal strength vectors reside in a high-dimensioned coordinate system. With the help of the so called Isomap-algorithm the dimensionality of this map can be reduced, and thus more easily processed. By embedding position-labeled strategic key points, we can automatically adjust the mapping to match the surveyed environment. The environment is thus learned in a semi-supervised way; gathering training points and embedding them in a two-dimensional manifold gives us a rough mapping of the measured environment. After a calibration phase, where the labeled key points in the training data are used to associate coordinates in the manifold representation with geographical locations, we can perform positioning using the adjusted map. This can be achieved through a traditional supervised learning process, which in our case is a simple nearest neighbors matching of a sampled signal strength vector. We deployed this system in two locations in the Kumpula campus in Helsinki, Finland. Results indicate that positioning based on the learned radio map can achieve good accuracy, especially in hallways or other areas in the environment where the WLAN signal is constrained by obstacles such as walls.