|Wp||WP2 WP3 WP4 WP5|
|Keywords||Playlist, statistics, history, visualisation, comparison|
Sonia likes to explore her playlist history, find interesting statistics and patterns and see how it compares to other music listeners.
Sonia has been using the Polifonia Music Player app  for some time. She likes using the intelligent agents to find out more about what she is listening to and extend her playlist with their recommendations. When listening to music she also likes to use companion apps that can give her statistics and visualisations of what she has been listening to such as last.fm , Obscurify , Stats for Spotify , MusicScape , Discover Quickly , Spotify Wrapped  and Apple Music Replay .
What she likes about these apps is that they can provide her with an overview of what she has been listening to over the past weeks, months or year and compare her listening experience against other music listeners. She is excited to see that Polifonia has launched its own companion app so connects it to the playlist history of her Polifonia Music Player app. As well as showing her statistics on what she has been listening to, such as her favourite albums, songs and genres, the app can use information supplied by the intelligent agents to provide more information about her listening history and visualise it in interesting ways.
The app shows her a visualization of harmonic patterns that commonly appear on her playlist. She selects one of the patterns and can see the breakdown of what types of songs it appears in. Although most of the songs it appears in are in the rock genre, it also features in some of her favourite classical pieces. She wonders whether that could be part of the reason she likes those classical pieces. She also follows a link to an article explaining the history of this chord progression and why it features on a number of rock tracks. She shares this with some of her friends.
She looks at a visualisation of the lyrics that appear in the songs she listens to. Phrases related to family and children are unusually common in the songs she listens to. In fact, the app tells her that these phrases appear on her playlist more than 90% of other music listeners. She wonders why this might be. As she drills down she can see that they have been used a number of times by some of her favourite bands. She had never noticed that before and wonders whether those bands need to look for new inspiration for their lyrics.
The app can also visualise her listening history by time and location. There is a large peak of songs released in 2012 and she remembers how much time she spent listening to new songs during that summer. She can also see how narrow her taste in music was during that time. The visualization shows her how her taste in music overall is far more eclectic than the 2012 songs and more eclectic than 95% of other listeners. She feels quite proud of this achievement and shares it on social media. She can also explore her playlist overlaid on a map. She is surprised by some of the cities that feature on her map due to being places where her played songs were recorded. She sees that many of her favourite indie tracks by different bands were recorded in the same city, in many cases in the same studio. She looks for other songs recorded in the same studio and adds them to her Polifonia playlist. She also notices that one of the music labels is based in that city and many of her favourite bands have performed in the city, sometimes at the same gig.