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Mood of Music is among the most relevant and commercially promising, yet challenging attributes for retrieval in large music collections. In this respect this article first provides a short overview on methods and performances in the field. While most past research so far dealt with low-level audio descriptors to this aim, this article reports on results exploiting information on middle-level as the rhythmic and chordal structure or lyrics of a musical piece. Special attention is given to realism and nonprototypicality of the selected songs in the database: all feature information is obtained by fully automatic preclassification apart from the lyrics which are automatically retrieved from on-line sources. Further more, instead of exclusively picking songs with agreement of several annotators upon perceived mood, a full collection of 69 double CDs, or 2 648 titles, respectively, is processed. Due to the severity of this task; different modelling forms in the arousal and valence space are investigated, and relevance per feature group is reported.