Augmented Granular Synthesis Method for GAN Latent Space with Redundancy Parameter

Koray Tahiroğlu, Miranda Kastemaa

Research output: Contribution to conferencePaperScientificpeer-review

26 Downloads (Pure)


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 in- strument 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.
Original languageEnglish
Publication statusPublished - 13 Sep 2022
MoE publication typeNot Eligible
EventConference on AI Music Creativity - Online, Virtual, Online, Japan
Duration: 13 Sep 202215 Sep 2022
Conference number: 3


ConferenceConference on AI Music Creativity
Abbreviated titleAICM
CityVirtual, Online
Internet address


  • GaN
  • music
  • NIME
  • AI and music
  • artificial intellegence


Dive into the research topics of 'Augmented Granular Synthesis Method for GAN Latent Space with Redundancy Parameter'. Together they form a unique fingerprint.
  • Co-Creative Artificial Intelligence of Music

    Tahiroglu, Koray (Recipient) & Sawhney, Nitin (Recipient), 1 Jan 2022

    Prize: Granted funding (public project funding)

Cite this