An experimental comparison of audio tempo induction algorithms

Abstract

We report on the tempo induction contest organized during the International Conference on Music Information Retrieval (ISMIR 2004) held at the University Pompeu Fabra in Barcelona, Spain, in October 2004. 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 organizer, in one of several allowed formats. No training data was provided. A total of 12 entries (representing the work of seven research teams) were evaluated, 11 of which are reported in this document. Results on the test set of 3199 instances were returned to the participants before they were made public. Anssi Klapuri's algorithm won the contest. This evaluation shows that tempo induction algorithms can reach over 80% accuracy for music with a constant tempo, if we do not insist on finding a specific metrical level. After the competition, the algorithms and results were analyzed in order to discover general lessons for the future development of tempo induction systems. One conclusion is that robust tempo induction entails the processing of frame features rather than that of onset lists. Further, we propose a new "redundant" approach to tempo induction, inspired by knowledge of human perceptual mechanisms, which combines multiple simpler methods using a voting mechanism. Machine emulation of human tempo induction is still an open issue. Many avenues for future work in audio tempo tracking are highlighted, as for instance the definition of the best rhythmic features and the most appropriate periodicity detection method. In order to stimulate further research, the contest results, annotations, evaluation software and part of the data are available at http://ismir2004.ismir.net/ISMIR-Contest.html

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“…In this manner, the usual strategy is to estimate the beat [30] and then aggregate the descriptor information corresponding to the same beat. This can be done independently of the descriptor used.…”

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“…Again, numerous approaches have been proposed, such as rule-based methods [33], adaptive oscillators [80], agent-based or multiple hypothesis trackers [37], filter-banks [29], dynamical systems [23] and probabilistic models [32]. Beat tracking methods are evaluated in [59,90]. The final step for metrical analysis consists of inferring the time signature, which indicates how beats are grouped and subdivided at respectively higher and lower metrical levels, and assigning (quantising) each onset and offset time to a position in this metrical structure [23].…”

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“…The evaluation is performed on the "ballroom dancer" testset (as was used for the ISMIR2004 tempo induction contest) [7]. This test-set is often used for evaluation since it contains music for which the music genre and the rhythm class are closely related.…”

“…Tempo estimation: we have used the measure proposed by [7], i.e. we measure the number of tracks for which the estimated tempo is within a 4% Tolerance Window of the annotated tempo (without considering octave errors);…”