miun.sePublications
Change search
Refine search result
1 - 1 of 1
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Payerl, Anders
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
    Tolka musiktecken från bilder: Optisk musikigenkänning med maskininlärning2018Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The objective of the project was to examine the possibility to use machine lear- ning without prior knowledge of machine learning or of mathematics and if that is possible also explore the possibility to use machine learning to interpret a picture of a piece of sheet music. The capacity of detecting notes from images of sheet music in the produced model was then compared to an existing pro- gram called Audiveris. The result became a model later used in a comparison with the program Audiveris. The comparison resulted in Audiveris finding al- most 100% of the notes but the new model only being able to detect about a third. The reasons for the big difference were probably: first that Audiveris has been in development for many years and secondly that the training data used for the new model wasn’t enough varied and complicated. To further increase the ability of the new model ́s skill the main point would be the need for more trai- ning data at the training of the model. Even then you need to supply a variation in the data ́s content and its degree of difficulty which is more varied then the first training data.

1 - 1 of 1
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf