|   | 
Details
   web
Record
Author (up) Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes
Title On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition Type Conference Article
Year 2021 Publication International Society for Music Information Retrieval Conference Abbreviated Journal
Volume Issue Pages 690-696
Keywords
Abstract Despite the latest advances in Deep Learning, the recognition of handwritten music scores is still a challenging endeavour. Even though the recent Sequence to Sequence(Seq2Seq) architectures have demonstrated its capacity to reliably recognise handwritten text, their performance is still far from satisfactory when applied to historical handwritten scores. Indeed, the ambiguous nature of handwriting, the non-standard musical notation employed by composers of the time and the decaying state of old paper make these scores remarkably difficult to read, sometimes even by trained humans. Thus, in this work we explore the incorporation of language models into a Seq2Seq-based architecture to try to improve transcriptions where the aforementioned unclear writing produces statistically unsound mistakes, which as far as we know, has never been attempted for this field of research on this architecture. After studying various Language Model integration techniques, the experimental evaluation on historical handwritten music scores shows a significant improvement over the state of the art, showing that this is a promising research direction for dealing with such difficult manuscripts.
Address Virtual; November 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ISMIR
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ TBK2021 Serial 3616
Permanent link to this record