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Jason Hockman: Fast vs slow: Learning tempo octaves from user data

  • Music Technology Student Colloquium
When Mar 15, 2011
from 12:30 PM to 01:30 PM
Where Room A512, New Music Building
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The widespread use of beat- and tempo-tracking methods in music information retrieval tasks has been marginalized due to undesirable sporadic results from these algorithms. While sensorimotor and listening studies have demonstrated the subjectivity and variability inherent to human performance of this task, MIR applications such as recommendation require more reliable output than available from present tempo estimation models. In this presentation, we present a initial investigation of tempo assessment based on the simple classification of whether the music is fast or slow. Through three experiments, we provide performance results of our method across two datasets, and demonstrate its usefulness in the pursuit of a reliable global tempo estimation.


Jason Hockman is a PhD Candidate under the advisement of Professor Ichiro Fujinaga in the Music Technology Area of the Schulich School of Music. He holds a M.M. from New York University (2007), and a B.A. in Sociology from Cornell University (2000). His main research interests lie in the automated determination of musical events and patterns (e.g., onset, beat, rhythm, and meter) from audio.



Our final colloquium will be on April 5, and we will have two special guests joining us. More details to follow!

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