FUSING BLOCK-LEVEL FEATURES FOR MUSIC SIMILARITY ESTIMATION

In this paper we present a novel approach to computing music similarity based on block-level features. We first introduce three novel block-level features — the Variance Delta Spectral Pattern (VDSP), the Correlation Pattern (CP) and the Spectral Contrast Pattern (SCP). Then we describe how to combine the extracted features into a single similarity function. A comprehensive evaluation based on genre classification experiments shows that the combined block-level similarity measure (BLS) is comparable, in terms of quality, to the best current method from the literature. But BLS has the important advantage of being based on a vector space representation, which directly facilitates a number of useful operations, such as PCA analysis, k-means clustering, visualization etc. We also show that there is still potential for further improve of music similarity measures by combining BLS with another stateof-the-art algorithm; the combined algorithm then outperforms all other algorithms in our evaluation. Additionally, we discuss the problem of album and artist effects in the context of similaritybased recommendation and show that one can detect the presence of such effects in a given dataset by analyzing the nearest neighbor classification results.