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.

Treball fi de màster de: Master in Sound and Music Computing

Supervisor: Pablo Alonso Jimenez

Supervisor: Dmitry Bogdanov