Towards a Class-Based Representation of Perceptual Tempo for Music Retrieval

Tempo is a common criterion by which humans describe and categorize music, and this has spawned a large amount of research in the field of automatic tempo estimation. Most tempo estimation systems focus mainly on detecting the temporal repetition and periodicity present within a signal, and represent tempo as a count of beats-per-minute (BPM). However, in real-world music retrieval applications such as music navigation and playlist generation, a rough perceptual representation of tempo may be more appropriate than a BPM representation. In this paper, the problem of tempo estimation is presented as a statistical classification problem. Four perceptual tempo classes are defined which correspond to rough semantic terms that average users may use to describe tempo. Statistical models of each class are built using low-level audio features. Experimental results show that the perceptual tempo class representation outperforms several conventional BPM-based tempo estimation systems when applied to the tasks of music navigation and playlist generation.