Unsupervised feature learning for Music Structural Analysis
Music Structural Analysis (MSA) algorithms analyze songs with the purpose of automatically retrieving their large-scale structure. They do so from a feature-based representation of the audio signal (e.g., MFCCs, chromagram), which is usually hand-designed for that specific application. In order to design a proper audio representation for MSA, we need to assess which musical properties are relevant for segmentation purposes (e.g., timbre, harmony); and we need to design signal processing strategies that can be used for capturing them. Deep learning techniques offer an alternative to this approach, as they are able to automatically find an abstract representation of the musical content. In this work we investigate their use in the task of Music Structural Analysis. In particular, we compare the performance of several state-of-the-art algorithms working with a collection of traditional descriptors and by descriptors that are extracted with a Deep Belief Network.