Dataset Open Access

MUSERC: Multi-Sensor Cello Recordings for Instantaneous Frequency Estimation

Fabian-Robert Stöter; Michael Müller; Bernd Edler

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  <identifier identifierType="DOI">10.5281/zenodo.1560651</identifier>
      <creatorName>Fabian-Robert Stöter</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-2534-1165</nameIdentifier>
      <creatorName>Michael Müller</creatorName>
      <creatorName>Bernd Edler</creatorName>
    <title>MUSERC: Multi-Sensor Cello Recordings for Instantaneous Frequency Estimation</title>
    <date dateType="Issued">2015-10-15</date>
  <resourceType resourceTypeGeneral="Dataset"/>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1560650</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Estimating the fundamental frequency (F0) of a signal is a well studied task in audio signal processing with many applications. If the F0 varies over time, the complexity increases, and it is also more difficult to provide ground truth data for evaluation. In this project we present a dataset of cello recordings addressing the lack of reference annotations for musical instruments. Besides audio data, we include sensor recordings capturing the finger position on the fingerboard which is converted into an instantaneous frequency estimate. This is similar to speech processing, where the electroglottograph (EGG) is able to capture the excitation signal of the vocal tract, which is then used to generate a reference instantaneous F0. Inspired by this approach, we included high speed video camera recordings to extract the excitation signal originating from the moving string. The derived data can be used to analyze vibratos &amp;mdash; a very commonly used playing style&lt;/p&gt;</description>
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