Abstract
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.
Highlights
Like with mood taxonomies there is still no agreed consensus on the learning algorithms to use for mood prediction
The choice highly depends on the selected mood model
Recent research, which deals with a four-class dimensional mood model [9, 10], uses Gaussian Mixture Models (GMM) as a base for a hierarchical classification system (HCS): at first a binary decision on arousal is made using only rhythm and timbre features
Summary
Audio encoding has enabled us to digitise our musical heritage and new songs are released digitally every day. As mass storage has become affordable, it is possible for everyone to aggregate a vast amount of music in personal collections. This brings with it the necessity to somehow organise this music. The established approach for this task is derived from physical music collections: browsing by artist and album is the best choice for searching familiar music for a specific track or release. Musical genres help to overview similarities in style among artists. This categorisation is quite ambiguous and difficult to carry out consistently
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