Search Results for author: Berker Banar

Found 3 papers, 2 papers with code

Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI

1 code implementation14 Nov 2023 Nick Bryan-Kinns, Bingyuan Zhang, Songyan Zhao, Berker Banar

This paper contributes a systematic examination of the impact that different combinations of Variational Auto-Encoder models (MeasureVAE and AdversarialVAE), configurations of latent space in the AI model (from 4 to 256 latent dimensions), and training datasets (Irish folk, Turkish folk, Classical, and pop) have on music generation performance when 2 or 4 meaningful musical attributes are imposed on the generative model.

Attribute Music Generation

Exploring XAI for the Arts: Explaining Latent Space in Generative Music

1 code implementation10 Aug 2023 Nick Bryan-Kinns, Berker Banar, Corey Ford, Courtney N. Reed, Yixiao Zhang, Simon Colton, Jack Armitage

We increase the explainability of the model by: i) using latent space regularisation to force some specific dimensions of the latent space to map to meaningful musical attributes, ii) providing a user interface feedback loop to allow people to adjust dimensions of the latent space and observe the results of these changes in real-time, iii) providing a visualisation of the musical attributes in the latent space to help people understand and predict the effect of changes to latent space dimensions.

Music Generation

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