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.
Highlights
With the proliferation of back-end warehouses of music metadata (e.g., AllMusic, Gracenote, Last.fm, MusicBrainz, The Echo Nest [1]), front-end online music stores (e.g., Amazon MP3, Google Play Music, iTunes, 7digital, Xbox Music [2]), and streaming music services (e.g., Deezer, MySpace Music, Napster, Rdio, Rhapsody, Spotify [3]) comes heretofore unparalleled opportunities to change the way music can be personalized for and delivered to target users with varying needs.PLOS ONE | DOI:10.1371/journal.pone.0110452 December 3, 2014Quantifying Auditory Temporal StabilityOne need, shared by both rehabilitation professionals and exercise enthusiasts, is the ability to create music playlists which facilitate the synchronization of complex motor actions with an auditory beat
Within the Stable Segment, most inter-beat interval (IBeI) differ by only a few ms, yielding low values for the IBeI variability statistics
The first caveat concerns the accuracy of the beat tracking algorithm; the second concerns the choice of thresholds used to define the Stable Segment
Summary
One need, shared by both rehabilitation professionals and exercise enthusiasts, is the ability to create music playlists which facilitate the synchronization of complex motor actions (e.g., walking) with an auditory beat. Auditory-motor synchronization has been deemed a human cultural universal [4] and a ‘‘diagnostic trait of our species’’ [5]. Even infants show perceptual sensitivity to [6] and coordinated motor engagement with [7] musical rhythms. The phenomenon of auditory entrainment (the dynamic altering of an ‘‘internal’’ periodic process or action generated by an organism in the presence of a periodic acoustic stimulus) remains an active topic for the field of music cognition [8,9,10,11,12,13,14]
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