Temporal Music Context Identification with User Listening Data

The times when music is played can indicate context for listeners. From the peaceful song for waking up each morning to the traditional song for celebrating a holiday to an up-beat song for enjoying the summer, the relationship between the music and the temporal context is clearly important. For music search and recommendation systems, an understanding of these relationships provides a richer environment to discover and listen. But with the large number of tracks available in music catalogues today, manually labeling track-temporal context associations is difficult, time consuming, and costly. This paper examines track-day contexts with the purpose of identifying relationships with specific music tracks. Improvements are made to an existing method for classifying Christmas tracks and a generalization to the approach is shown that allows automated discovery of music for any day of the year. Analyzing the top 50 tracks obtained from this method for three well-known holidays, Halloween, Saint Patrick’s Day, and July 4th, precision@50 was 95%, 99%, and 73%, respectively.