Preprint Article Version 1 This version is not peer-reviewed

Automatic Neume Transcription of Medieval Music Manuscripts using CNN/LSTM-Networks and the segmentation-free CTC-Algorithm

Version 1 : Received: 14 January 2020 / Approved: 15 January 2020 / Online: 15 January 2020 (12:11:25 CET)

How to cite: Wick, C.; Puppe, F. Automatic Neume Transcription of Medieval Music Manuscripts using CNN/LSTM-Networks and the segmentation-free CTC-Algorithm. Preprints 2020, 2020010149 (doi: 10.20944/preprints202001.0149.v1). Wick, C.; Puppe, F. Automatic Neume Transcription of Medieval Music Manuscripts using CNN/LSTM-Networks and the segmentation-free CTC-Algorithm. Preprints 2020, 2020010149 (doi: 10.20944/preprints202001.0149.v1).

Abstract

The automatic recognition of scanned Medieval manuscripts still represents a challenge due to degradation, non standard layouts, or notations. This paper focuses on the Medieval square notation developed around the 11th century which is composed of staff lines, clefs, accidentals, and neumes which are basically connected single notes. We present a novel approach to tackle the automatic transcription by applying CNN/LSTM networks that are trained using the segmentation-free CTC-loss-function which considerably facilitates the GT-production. For evaluation, we use three different manuscripts and achieve a dSAR of 86.0% on the most difficult book and 92.2% on the cleanest one. To further improve the results, we apply a neume dictionary during decoding which yields a relative improvement of about 5%.

Subject Areas

Optical Music Recognition; Historical Document Analysis; Medieval manuscripts; neume notation; CNN; LSTM; CTC

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