A Comparison of Signal Based Music Recommendation to Genre Labels, Collaborative Filtering, Musicological Analysis, Human Recommendation and Random Baseline

The emergence of the Internet as today’s primary medium of music distribution has brought about demands for fast and reliable ways to organize, access, and discover music online. To date, many applications designed to perform such tasks have risen to popularity; each relies on a specific form of music metadata to help consumers discover songs and artists that appeal to their tastes. Very few of these applications, however, analyze the signal waveforms of songs directly. This low-level representation can provide dimensions of information that are inaccessible by metadata alone. To address this issue, we have implemented signalbased measures of musical similarity that have been optimized based on their correlations with human judgments. Furthermore, multiple recommendation engines relying on these measures have been implemented. These systems recommend songs to volunteers based on other songs they find appealing. Blind experiments have been conducted in which volunteers rate the systems’ recommendations along with recommendations of leading online music discovery tools (Allmusic which uses genre labels, Pandora which uses musicological analysis, and Last.fm which uses collaborative filtering), random baseline recommendations, and personal recommendations by the first author. This paper shows that the signal-based engines perform about as well as popular, commercial, state-of-the-art systems.