Gnmid14

Summers, Cameron; Tronel, Greg; Cramer, Jason; Vartakavi, Aneesh; Popp, Phillip

doi:10.1145/2911451.2914679

Cited by 2 publications

(2 citation statements)

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“…The dataset analyzed here is comparable in size to other recently released industrial datasets for music research. For example, the #nowplaying dataset currently exceeds 56 million tweets (Zangerle et al, 2014), while Gracenote's GNMID14 dataset exceeds 100 million music identification matches (Summers et al, 2016). Shazam data are also ubiquitous, meaning that they reflect music discovery in a variety of contexts throughout daily life.…”

Section: Discussionmentioning

confidence: 99%

“…Music consumption and sharing has also been approached using Spotify URLs shared via Twitter (Pichl et al, 2014, 2015) and music download data from the MixRadio database (Bansal and Woolhouse, 2015). A number of these studies have contributed or made use of publicly available research corpuses, including the Million Musical Tweets Dataset, containing temporal and geographical information linked to music-related tweets (Hauger et al, 2013); the continually updated #nowplaying dataset of music-related tweets (Zangerle et al, 2014); and Gracenote's GNMID14 dataset, which includes annotated music identification matches (Summers et al, 2016). …”

Section: Introductionmentioning

confidence: 99%

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Characterizing Listener Engagement with Popular Songs Using Large-Scale Music Discovery Data

et al. 2017

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Music discovery in everyday situations has been facilitated in recent years by audio content recognition services such as Shazam. The widespread use of such services has produced a wealth of user data, specifying where and when a global audience takes action to learn more about music playing around them. Here, we analyze a large collection of Shazam queries of popular songs to study the relationship between the timing of queries and corresponding musical content. Our results reveal that the distribution of queries varies over the course of a song, and that salient musical events drive an increase in queries during a song. Furthermore, we find that the distribution of queries at the time of a song's release differs from the distribution following a song's peak and subsequent decline in popularity, possibly reflecting an evolution of user intent over the “life cycle” of a song. Finally, we derive insights into the data size needed to achieve consistent query distributions for individual songs. The combined findings of this study suggest that music discovery behavior, and other facets of the human experience of music, can be studied quantitatively using large-scale industrial data.

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Section: Discussionmentioning

confidence: 99%