<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>3029</id>
    <completedYear>2025</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>438</pageFirst>
    <pageLast>445</pageLast>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>The International Computer Music Association</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-07-07</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription</title>
    <abstract language="eng">Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription.</abstract>
    <parentTitle language="eng">Proceedings of the 50th International Computer Music Conference 2025</parentTitle>
    <enrichment key="PeerReviewed">Ja</enrichment>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="conference_date">June 8-14, 2025</enrichment>
    <enrichment key="conference_place">Boston, USA</enrichment>
    <enrichment key="conference_title">The 50th International Computer Music Conference 2025</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Anna Hamberger</author>
    <author>Sebastian Murgul</author>
    <author>Jochen Schmidt</author>
    <author>Michael Heizmann</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
</export-example>
