Effects of Album and Artist Filters in Audio Similarity Computed for Very Large Music Databases

Abstract

In music information retrieval, one of the central goals is to automatically recommend music to users based on a query song or query artist. This can be done using expert knowledge (e.g., www.pandora.com), social meta-data (e.g., www.last.fm), collaborative filtering (e.g., www.amazon.com/mp3), or by extracting information directly from the audio (e.g., www.muffin.com). In audio-based music recommendation, a wellknown effect is the dominance of songs from the same artist as the query song in recommendation lists. This effect has been studied mainly in the context of genre-classification experiments. Because no ground truth with respect to music similarity usually exists, genre classification is widely used for evaluation of music similarity. Each song is labelled as belonging to a music genre using, e.g., advice of a music expert. High genre classification results indicate good similarity measures. If, in genre classification experiments, songs from the same artist are allowed in both training and test sets, this can lead to over-optimistic results since usually all songs from an artist have the same genre label. It can be argued that in such a scenario one is doing artist classification rather than genre classification. One could even speculate that the specific sound of an album (mastering and production effects) is being classified. In Pampalk, Flexer, and Widmer (2005) the use of a so-called “artist filter” that ensures that a given artist’s songs are either all in the training set, or all in the test set, is proposed. Those authors found that the use of such an artist filter can lower the