Interactive machine learning for music classification

Audio embeddings are a promising approach to music representation, in part thanks to their ability to extract complex patterns from audio data; the predictive power of audio embeddings is utilized for a semantically meaningful, two-dimensional visualization of music data in a user interface (UI) which has been developed as part of this thesis research. As a contribution to ongoing research on the intersection between music information retrieval (MIR) and interactive machine learning (IML), the UI allows users to iteratively train a classifier for numerous audio classification tasks. As part of this research, the certainty-based class prediction uncertainty (CPU) heuristic, and the dataset coverage (DC) heuristic are proposed; these heuristics are shown to identify informative samples in music collections, and their efficiency is objectively evaluated by means of simulated, iterative active learning (AL) classification tasks for 6 different embedding-dataset pairs. The objective evaluations have shown promising results, in which high classification accuracies are shown to be achieved in fewer iterations in AL classification tasks.