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We present a method for searching databases of symbolically represented polyphonic music that exploits advantages of transportation distances such as continuity and partial matching in the pitch dimension. By segmenting queries and database documents, we also gain partial matching in the time dimension. Thus, we can find short queries in long database documents, and have a method more robust against pitch and tempo fluctuations in the queries or database documents than we would with transportation distances alone. We compare our method with three algorithms from the C-Brahms project by Lemström et al. and with PROMS by Clausen et al. and find that our method is more generally usable, retrieves a higher number of relevant documents than all three compared algorithms, and that it is faster than C-Brahms. This is the first comparative study of these algorithms involving a large database with about half a million of documents.

The cold start problem for new users or items is a great challenge for recommender systems. New items can be positioned within the existing items using a similarity metric to estimate their ratings. However, the calculation of similarity varies by domain and available resources. In this paper, we propose a content-based music recommender system which is based on a set of attributes derived from psychological studies of music preference. These five attributes, namely, Mellow, Unpretentious, Sophisticated, Intense and Contemporary (MUSIC), better describe the underlying factors of music preference compared to music genre. Using 249 songs and hundreds of ratings and attribute scores, we first develop an acoustic content-based attribute detection using auditory modulation features and a regression by sparse representation. We then use the estimated attributes in a cold start recommendation scenario. The proposed content-based recommendation significantly outperforms genre-based and user-based recommendation based on the root-mean-square error. The results demonstrate the effectiveness of these attributes in music preference estimation. Such methods will increase the chance of less popular but interesting songs in the long tail to be listened to.

For the RISM A/II collection of musical incipits (short extracts of scores, taken from the beginning), we have established a ground truth based on the opinions of human experts. It contains correctly ranked matches for a set of given queries. These ranked lists contain groups of documents whose ranks were not significantly different. In other words, they are only partially ordered. To make use of the available information for measuring the quality of retrieval results, we introduce the "average dynamic recall" (ADR) that averages the recall among a dynamic set of relevant documents, taking into account the fact that the ground truth reliably orders groups of matches, but not always individual matches. Dynamic recall measures how many of the documents that should have appeared before or at a given position in the result list actually have appeared. ADR at a given position averages this measure up to the given position. Our measure was first used at the MIREX 2005 Symbolic Melodic Similarity contest.