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. Preprints2020, 2020010149. https://doi.org/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. https://doi.org/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. Preprints2020, 2020010149. https://doi.org/10.20944/preprints202001.0149.v1
APA Style
Wick, C., & Puppe, F. (2020). Automatic Neume Transcription of Medieval Music Manuscripts using CNN/LSTM-Networks and the segmentation-free CTC-Algorithm. Preprints. https://doi.org/10.20944/preprints202001.0149.v1
Chicago/Turabian Style
Wick, C. and Frank Puppe. 2020 "Automatic Neume Transcription of Medieval Music Manuscripts using CNN/LSTM-Networks and the segmentation-free CTC-Algorithm" Preprints. https://doi.org/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%.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
