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Quantifying Auditory Temporal Stability in a Large Database of Recorded Music

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  • Robert J Ellis
  • Zhiyan Duan
  • Ye Wang

Abstract

“Moving to the beat” is both one of the most basic and one of the most profound means by which humans (and a few other species) interact with music. Computer algorithms that detect the precise temporal location of beats (i.e., pulses of musical “energy”) in recorded music have important practical applications, such as the creation of playlists with a particular tempo for rehabilitation (e.g., rhythmic gait training), exercise (e.g., jogging), or entertainment (e.g., continuous dance mixes). Although several such algorithms return simple point estimates of an audio file’s temporal structure (e.g., “average tempo”, “time signature”), none has sought to quantify the temporal stability of a series of detected beats. Such a method-a “Balanced Evaluation of Auditory Temporal Stability” (BEATS)–is proposed here, and is illustrated using the Million Song Dataset (a collection of audio features and music metadata for nearly one million audio files). A publically accessible web interface is also presented, which combines the thresholdable statistics of BEATS with queryable metadata terms, fostering potential avenues of research and facilitating the creation of highly personalized music playlists for clinical or recreational applications.

Suggested Citation

  • Robert J Ellis & Zhiyan Duan & Ye Wang, 2014. "Quantifying Auditory Temporal Stability in a Large Database of Recorded Music," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-24, December.
    • Handle: RePEc:plo:pone00:0110452
    DOI: 10.1371/journal.pone.0110452
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    Cited by:

    1. Robert J Ellis & Yee Sien Ng & Shenggao Zhu & Dawn M Tan & Boyd Anderson & Gottfried Schlaug & Ye Wang, 2015. "A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.

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