Deep Layered Learning in MIR
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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 structurally flexible processing chain is necessary to extract high-level features from a spectrogram representation. Mid-level representations such as tones, pitched onsets, chords, and beats are fundamental building blocks of music. This paper discusses how these can be used as intermediate representations in MIR to facilitate deep processing that generalizes well: each music concept is predicted individually in learning modules that are connected through latent representations in a directed acyclic graph. It is suggested that this strategy for inference, defined as deep layered learning (DLL), can help generalization by (1) - enforcing the validity of intermediate representations during processing, and by (2) - letting the inferred representations establish disentangled structures that support high-level invariant processing. A background to DLL and modular music processing is provided, and relevant concepts such as pruning, skip-connections, and layered performance supervision are reviewed.
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