Deep Layered Learning in MIR
This addresses the problem of handling structurally complex learning in MIR for researchers and practitioners, but it appears incremental as it builds on existing modular and deep learning approaches.
The paper tackles the challenge of modeling complex hierarchical music structures in music information retrieval (MIR) by proposing deep layered learning (DLL), which uses intermediate representations like tones and chords to improve generalization, and shows in a pitch tracking test that it makes offsets easier to detect when guided by extracted fundamental frequencies.
Deep learning has boosted the performance of many music information retrieval (MIR) systems in recent years. Yet, the complex hierarchical arrangement of music makes end-to-end learning hard for some MIR tasks - a very deep and flexible processing chain is necessary to model some aspect of music audio. Representations involving tones, chords, and rhythm are fundamental building blocks of music. This paper discusses how these can be used as intermediate targets and priors in MIR to deal with structurally complex learning problems, with learning modules connected in a directed acyclic graph. It is suggested that this strategy for inference, referred to as deep layered learning (DLL), can help generalization by (1) - enforcing the validity and invariance of intermediate representations during processing, and by (2) - letting the inferred representations establish the musical organization to support higher-level invariant processing. A background to modular music processing is provided together with an overview of previous publications. Relevant concepts from information processing, such as pruning, skip connections, and performance supervision are reviewed within the context of DLL. A test is finally performed, showing how layered learning affects pitch tracking. It is indicated that especially offsets are easier to detect if guided by extracted framewise fundamental frequencies.