01416nas a2200133   4500008004100000245001900041210001900060260001300079520107000092100002301162700002101185700002001206856005601226       2016                        eng d00aAudio Ergo Sum0 aAudio Ergo Sum  bSpringer3 aNobody can state “Rock is my favorite genre” or “David Bowie is my favorite artist”. We defined a Personal Listening Data Model able to capture musical preferences through indicators and patterns, and we discovered that we are all characterized by a limited set of musical preferences, but not by a unique predilection. The empowered capacity of mobile devices and their growing adoption in our everyday life is generating an enormous increment in the production of personal data such as calls, positioning, online purchases and even music listening. Musical listening is a type of data that has started receiving more attention from the scientific community as consequence of the increasing availability of rich and punctual online data sources. Starting from the listening of 30k Last.Fm users, we show how the employment of the Personal Listening Data Models can provide higher levels of self-awareness. In addition, the proposed model will enable the development of a wide range of analysis and musical services both at personal and at collective level.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/audio-ergo-sum