Many tasks in music information retrieval, such as recommendation, and playlist generation for online radio, fall naturally into the query-by-example setting, wherein a user queries the system by providing a song, and the system responds with a list of relevant or similar song recommendations. Such applications ultimately depend on the notion of similarity between items to produce high-quality results. Current state-of-the-art systems employ collaborative filter methods to represent musical items, effectively comparing items in terms of their constituent users. While collaborative filter techniques perform well when historical data is available for each item, their reliance on historical data impedes performance on novel or unpopular items. To combat this problem, practitioners rely on content-based similarity, which naturally extends to novel items, but is typically out-performed by collaborative filter methods. In this article, we propose a method for optimizing contentbased similarity by learning from a sample of collaborative filter data. The optimized content-based similarity metric can then be applied to answer queries on novel and unpopular items, while still maintaining high recommendation accuracy. The proposed system yields accurate and efficient representations of audio content, and experimental results show significant improvements in accuracy over competing content-based recommendation techniques.
Early studies of musical similarity followed the general strategy of first devising a model of audio content ( , spectral clusters @cite_37 or Gaussian mixture models @cite_7 ), applying some reasonable distance function ( , earth-mover's distance or Kullback-Leibler divergence), and then evaluating the proposed similarity model against some source of ground truth. Logan and Salomon @cite_37 and Aucouturier and Pachet @cite_7 evaluated against three notions of similarity between songs: same artist, same genre, and human survey data. Artist or genre agreement entail strongly binary notions of similarity, which due to symmetry and transitivity may be unrealistically coarse in practice. Survey data can encode subtle relationships between items, for example, triplets of the form @cite_7 @cite_16 @cite_21 . However, the expressive power of human survey data comes at a cost: while artist or genre meta-data is relatively inexpensive to collect for a set of songs, similarity survey data may require human feedback on a quadratic (for pairwise ratings) or cubic (for triplets) number of comparisons between songs.
@cite_37 @cite_16 @cite_21 @cite_7
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The present invention computer method and apparatus determines music similarity by generating a K-means (instead of Gaussian) cluster signature and a beat signature for each piece of music. The beat of the music is included in the subsequent distance measurement. would be interesting and valuable to devise an automatic measure of the similarity between two musicians based only on an analysis of their recordings. To develop such a measure, however, presupposes some 'ground truth' training data describing the actual similarity between certain pairs of artists that constitute the desired output of the measure. Since artist similarity is wholly subjective, such data is not easily obtained. In this paper, we describe several attempts to construct a full matrix of similarity measures between a set of some 400 popular artists by regularizing limited subjective judgment data. We also detail our attempts to evaluate these measures by comparison with direct subjective similarity judgments collected via a web- based survey in April 2002. Overall, we find that subjective artist similarities are quite variable between users—casting doubt on the concept of a single 'ground truth'. Our best measure, however, gives reasonable agreement with the subjective data, and forms a useable stand-in. In addition, our evaluation methodology may be useful for comparing other measures of artist similarity. Electronic Music Distribution (EMD) is in demand of robust, automatically extracted music descriptors. We introduce a timbral similarity measures for comparing music titles. This measure is based on a Gaussian model of cepstrum coefficients. We describe the timbre extractor and the corresponding timbral similarity relation. We describe experiments in assessing the quality of the similarity relation, and show that the measure is able to yield interesting similarity relations, in particular when used in conjunction with other similarity relations. We illustrate the use of the descriptor in several EMD applications developed in the context of the Cuidado European project.