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  <doc>
    <id>1201</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>9</pageLast>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>workingpaper</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-11-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">AI Generated Music Using Speech Emotion Recognition</title>
    <abstract language="eng">This study aims to compare two different implementations of speech emotion recognition models.The emphasis is directed towards evaluating their efficacy in capturing and characterizing dialogues&#13;
portrayed by actors within a film scene to create suitable musical intervals. The goal of the overarching research intends to derive indications to enhance the compositional process of film scores by recognizing the emotion in a particular scene. Based on established deep learning models, the study delves into the exploration of two distinct emotion classification metrics: The Six Emotion Prediction and the Valence/Arousal/Dominance Prediction. To facilitate a comparative analysis, a preliminary study an a following survey is deployed. The preliminary study confirms a significant difference in the generated MIDI data. For this reason, a survey is essential to detect the better fitting algorithm. Participants are tasked to rate the affective suitability of eight generated interval sequences to the corresponding film scenes. The Suitability is verified quantitatively using a bidirectional rating system. Both model assessments are conducted within a uniform sound design, thus ensuring unbiased conditions for evaluation. Upon a thorough examination of our extensive analysis, a preference for method A becomes increasingly evident.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:92-opus4-12016</identifier>
    <identifier type="doi">10.34646/thn/ohmdok-1201</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Roberto Murru</author>
    <author>Jonas Krug</author>
    <author>Tom Schmid</author>
    <author>Garri Steba</author>
    <author>Giorgio Giacinto</author>
    <author>Alexander von Hoffmann</author>
    <collection role="institutes" number="">Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik</collection>
    <collection role="institutes" number="">Ohm User Experience Center (Ohm-UX)</collection>
    <thesisPublisher>Technische Hochschule Nürnberg Georg Simon Ohm</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-ohm/files/1201/AIGeneratedMusicUsingSpeechEmotionRecognition.pdf</file>
  </doc>
  <doc>
    <id>4148</id>
    <completedYear>2025</completedYear>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>other</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2025-07-31</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">LEGO® SERIOUS PLAY® als Kreativmethode in studentischen Vorträgen</title>
    <abstract language="deu">Wie gelingt es, dass in einer Freitagsveranstaltung mit Anwesenheitspflicht und vielen studentischen Vorträgen die Studierenden nicht nur gern teilnehmen, sondern sie auch den Vorträgen aufmerksam folgen können?&#13;
&#13;
Prof. von Hoffmann hat zu diesem Zweck die Methode des LEGO® SERIOUS PLAY® ausgetestet und schildert in diesem Blog Artikel seine Erfahrungen.</abstract>
    <parentTitle language="deu">Portal Digitale Lehre / Praxisbeispiele</parentTitle>
    <identifier type="url">https://leko.service.th-nuernberg.de/portal-digitale-lehre/praxisbeispiele/lego-serious-play-als-kreativmethode-in-studentischen-vortraegen/</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>Alexander von Hoffmann</author>
    <author>Chris Herrmann</author>
    <author>Jonas Krug</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Didaktik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>LEGO® SERIOUS PLAY®</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Praxis Seminar</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Kreativmethode</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Lehrmethode</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Blog</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik</collection>
    <collection role="Forschungsschwerpunkt" number="5">Digitalisierung &amp; Künstliche Intelligenz</collection>
    <collection role="institutes" number="">Ohm User Experience Center (Ohm-UX)</collection>
    <collection role="institutes" number="">Lehr- und Kompetenzentwicklung</collection>
  </doc>
</export-example>
