Neural Audio-To-Score Music Transcription For Unconstrained Polyphony Using Compact Output Representations

Neural Audio-to-Score (A2S) Music Transcription systems have shown promising results with pieces containing a fixed number of voices. However, they still exhibit fundamental limitations that constrain their applicability in wider scenarios. This work aims at tackling two of them: we introduce a novel output representation which addresses shortcomings related to the sequence-based A2S recognition framework and we report a first approximation to dealing with unconstrained polyphony. This is validated on a Convolutional Recurrent Neural Network (CRNN) with Connectionist Temporal Classification (CTC) A2S scheme using synthetic audio from string quartets and piano sonatas with intricate polyphonic mixtures. Our results, which improve fixed-polyphony state-of-the-art rates, may be considered a reference for future A2S works dealing with an unconstrained number of voices.