kth.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.
    Falk, Simon
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Sturm, Bob
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Ahlbäck, Sven
    DoReMIR Music Research AB.
    Automatic legato transcription based on onset detection2023In: SMC 2023: Proceedings of the Sound and Music Computing Conference 2023, Sound and Music Computing Network , 2023, p. 214-221Conference paper (Refereed)
    Abstract [en]

    This paper focuses on the transcription of performance expression and in particular, legato slurs for solo violin performance. This can be used to improve automatic music transcription and enrich the resulting notations with expression markings. We review past work in expression detection, and find that while legato detection has been explored its transcription has not. We propose a method for demarcating the beginning and ending of slurs in a performance by combining pitch and onset information produced by ScoreCloud (a music notation software with transcription capabilities) with articulated onsets detected by a convolutional neural network. To train this system, we build a dataset of solo bowed violin performance featuring three different musicians playing several exercises and tunes. We test the resulting method on a small collection of recordings of the same excerpt of music performed by five different musicians. We find that this signal-based method works well in cases where the acoustic conditions do not interfere largely with the onset strengths. Further work will explore data augmentation for making the articulation detection more robust, as well as an end-to-end solution. 

    Download full text (pdf)
    fulltext
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