Data-Driven Song Recognition Estimation Using Collective Memory Dynamics Models

Cultural products such as music tracks intend to be appreciated and recognized by a portion of the audience. However, no matter how highly recognized a song might be at the beginning of its life, its recognition will inevitably and progressively decay. The mechanism that governs this decreasing trajectory could be modelled as a forgetting curve or a collective memory decay process. Here, we propose a composite model, termed T-REC, that involves chart data, YouTube views, Spotify popularity of tracks and forgetting curve dynamics with the purpose of estimating song recognition levels. We also present a comparative study, involving state-of-the-art and baseline models based on ground truth data from a survey that we conducted regarding the recognition level of 100 songs in Sweden. Our method is found to perform best among this ensemble of models. A remarkable finding of our study pertains to the role of the number of weeks a song remains in the charts, which is found to be a major factor for the accurate estimation of the song recognition level.