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A central problem in music information retrieval is finding suitable representations which enable efficient and accurate computation of musical similarity and identity. Low level audio features are ideal for calculating identity, but are of limited use for similarity measures, as many aspects of music can only be captured by considering high level features.(More)
The goal of this contest was to evaluate some state-of-the-art algorithms in the task of inducing the basic tempo (as a scalar, in beats per minute) from musical audio signals. To our knowledge, this is the first published large scale cross-validation of audio tempo induction algorithms. Participants were invited to submit algorithms to the contest(More)
Organising or browsing music collections in a musically meaningful way calls for tagging the data in terms of e.g. rhythmic, melodic or harmonic aspects, among others. In some cases, such metadata can be extracted automatically from musical files; in others, a trained listener must extract it by hand. In this article, we consider a specific set of rhythmic(More)
Groove is often described as the experience of music that makes people tap their feet and want to dance. A high degree of consistency in ratings of groove across listeners indicates that physical properties of the sound signal contribute to groove (Madison, 2006). Here, correlations were assessed between listeners' ratings and a number of quantitative(More)
We report experiments on the use of standard natural language processing (NLP) tools for the analysis of music lyrics. A significant amount of music audio has lyrics. Lyrics encode an important part of the semantics of a song, therefore their analysis complements that of acoustic and cultural metadata and is fundamental for the development of complete music(More)
—In this paper, we propose a method that can identify challenging music samples for beat tracking without ground truth. Our method, motivated by the machine learning method " selective sampling " , is based on the measurement of mutual agreement between beat sequences. In calculating this mutual agreement we show the critical influence of different(More)