In this paper, we investigate the impact of machine learning algorithms in the development of automatic music classification models aiming to capture genres distinctions. The study of genres as bodies of musical items aggregated according to subjective and local criteria requires corresponding inductive models of such a notion. This process can be thus modeled as an example-driven learning task...

This study examined the way in which television genres in the Netherlands make use of additional communication channels in terms of interactivity and genre modification and whether the availability of additional communication channels in genres corresponds to audience age. Expert interviews were held with representatives of Dutch broadcasting organizations and a secondary analysis of Audience R...

Musical onset detection is a key component in any beat tracking system. Existing algorithms for onset detection usually rely on short time Fourier transform (STFT). Of numerous existing methods to detect musical onsets, a common limitation is the genre-specific performance. For example, accurate onset locations are not usually identified for genres such as classical music and opera music. In th...

We present a new approach to defining film genre based on implicit ideals. When viewers rate the likability of a film, they indirectly express their ideal of what a film should be. Across six studies we investigate the category structure that emerges from likability ratings and the category structure that emerges from the features of film. We further compare these data-driven category structure...

In this paper, we analyze web-downloaded data on people sharing their music library. By attributing to each music group usual music genres (Rock, Pop...), and analysing correlations between music groups of different genres with percolation-idea based methods, we probe the reality of these subdivisions and construct a music genre cartography, with a tree representation. We also show the diversit...