[1S4-IS-1] Knowledge engineering

[1S4-IS-1-03] Analyzing the Relationship Between Weather and Music Preference

〇Yangyang Zhou¹, Kongmeng Liew¹, Shuntaro Yada¹, Shoko Wakamiya¹, Eiji Aramaki¹ (1. NARA INSTITUTE OF SCIENCE AND TECHNOLOGY)

Regular

Keywords:music preferences, data mining, playlist, K-means, weather

Previous research has considered individual or personal level influences of music preference extensively, but there has been less research considering environmental variables like weather patterns. In this paper, we aim to find whether weather patterns are related to music preference at the city level. Music preference was estimated through Top25 tracks from 106 cities, and corresponding weather data was collected from Apple Music and OpneWeather, respectively. We then compared weather variables to acoustic features (obtained from Spotify) for these tracks by comparing their K-means clusters (Adjust Rand Index) for both sets of data, but found no relationship. Following which, we analyzed from a microscopic perspective and found that mean daily temperature was negatively correlated with the danceability, energy, and tempo features, but positively correlated with loudness. In conclusion, we found only a limited relationship between weather and music preference at the city level: a significant relationship was observed for acoustic features pertaining to emotion and daily temperature. These findings complement the existing literature on the relationship between environmental variables and music preference, clarifying the relationship between ecology and music preference at the city level, and may potentially have implications for music recommendation algorithm development.