Adaptive methods for user-centered organization of music collections

Music Information Retrieval (MIR) systems have to deal with multi-faceted music information and very heterogeneous users. Especially when the task is to organize a music collection, the diverse perspectives of users caused by their different level of expertise, musical background or taste pose a great challenge. This challenge is addressed in this book by proposing adaptive methods for several elements of MIR systems: Data-adaptive feature extraction techniques are described that aim to increase the quality and robustness of the information extracted from audio recordings. The classical genre classification problem is approached from a novel user-centric perspective – promoting the idea of idiosyncratic genres that better reflect a user’s personal listening habits. An adaptive visualization technique for exploration and organization of music collections is elaborated that especially addresses the common and inevitable problem of projection errors introduced by dimensionality reduction approaches. Furthermore, it is outlined how this technique can be applied to facilitate serendipitous music discoveries in a recommendation scenario and to enable novel gaze-supported interaction techniques. Finally, a general approach for adaptive music similarity is presented which serves as the core of many adaptive MIR applications. Application prototypes demonstrate the usability of the described approaches.