Conference paper Open Access

Augmented Granular Synthesis Method for GAN Latent Space with Redundancy Parameter

Tahiroğlu, Koray; Kastemaa, Miranda

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="">
  <controlfield tag="005">20220918022629.0</controlfield>
  <controlfield tag="001">7088414</controlfield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="d">13-15 September 2022</subfield>
    <subfield code="g">AIMC 2022</subfield>
    <subfield code="a">The 3rd Conference on AI Music Creativity</subfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Department of Art and Media, Aalto University School of Arts, Design and Architecture</subfield>
    <subfield code="a">Kastemaa, Miranda</subfield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">817026</subfield>
    <subfield code="z">md5:20f207425f640a40a04fe1b7978d7a63</subfield>
    <subfield code="u">ğlu_2022__Augmented_Granular_Synthesis_Method_for_GAN_Latent_Space_with_Redundancy_Parameter.pdf</subfield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2022-09-17</subfield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o"></subfield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Department of Art and Media, Aalto University School of Arts, Design and Architecture</subfield>
    <subfield code="a">Tahiroğlu, Koray</subfield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Augmented Granular Synthesis Method for GAN Latent Space with Redundancy Parameter</subfield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u"></subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2"></subfield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;In this paper we introduce an augmented granular sound synthesis method for a GAN latent space exploration in audio domain. We use the AI-terity musical instrument for sound generating events in which the neural network (NN) parameters are optimised and then the features are used as a basis to generate new sounds. The exploration of a latent space is realised by creating a latent space through the original features of the training data set and finding the corresponding audio feature of the vector points in this space. Our proposed sound synthesis method can achieve multiple audio generation and sound synthesising events simultaneously without interrupting the playback grains. To do that we introduce redundancy parameter that schedules additional buffer slots divided from a large buffer slot, allowing multiple latent space vector points to be used in granular synthesis, in GPU real-time. Our implementation demonstrates that augmented buffer schedule slots can be used as a feature for a sound synthesis method to explore GAN-latent sound synthesis of granular-musical events with multiple generated audio samples without interrupting the granular musical features of the synthesis method.&lt;/p&gt;</subfield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.7088413</subfield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="b">AIMC</subfield>
    <subfield code="t">Proceedings of the 3rd Conference on AI Music Creativity</subfield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.7088414</subfield>
    <subfield code="2">doi</subfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">conferencepaper</subfield>
All versions This version
Views 5252
Downloads 3535
Data volume 28.6 MB28.6 MB
Unique views 5050
Unique downloads 3232


Cite as