Currently the following Musly music similarity methods are available:


As published in M. Mandel and D. Ellis: Song-level features and support vector machines for music classification. (In the proceedings of the 6th International Conference on Music Information Retrieval, ISMIR, 2005). This measure computes the MFCC representation of each song to estimate a single Gaussian. The Gaussians are compared with the Kullback-Leiber divergence and make up the similarity.

Timbre (Musly default)

Tweaks the Mandel-Ellis similarity measure for best results. We use a 25 MFCCs representation, the Jensen-Shannon divergence and, most importantly, we normalize the similarities with Mutual Proximity (D. Schnitzer et al.: Using mutual proximity to improve content-based audio similarity. In the proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR, 2011).


A commercial music similarity measure of high similarity quality and scalability. Can be used with millions of tracks. More information about it can be found on the OFAI website.

Evaluation of the Methods (January 2014)

We perform automatic music genre classification experiments to evaluate Musly. A standardized leave-one-out nearest neighbor classification experiment is performed with multiple standard music collections used in MIR research. The results for Musly 0.1 are displayed in the graph above. If available, we apply an artist filter. All classification experiments can be easily repeated by using the Musly command line application with the respective collection (musly -E).