On the Automatic Identification of Music for Common Activities.

In this paper, we address the challenge of identifying music suitable to accompany typical daily activities. We first derive a list of common activities by analyzing social media data. Then, an automatic approach is proposed to find music for these activities. Our approach is inspired by our experimentally acquired findings (a) that genre and instrument information, i.e., as appearing in the textual metadata, are not sufficient to distinguish music appropriate for different types of activities, and (b) that existing content-based approaches in the music information retrieval community do not overcome this insufficiency. The main contributions of our work are (a) our analysis of the properties of activity-related music that inspire our use of novel high-level features, e.g., drop-like events, and (b) our approach's novel method of extracting and combining low-level features, and, in particular, the joint optimization of the time window for feature aggregation and the number of features to be used. The effectiveness of the approach method is demonstrated in a comprehensive experimental study including failure analysis.