The research investigates the performance of autoencoders in the compression of structured data. Autoencoders are a prominent model in many branches of machine learning and data compression. The researchers prove…
Continue readingThis article discusses a novel model, T5VQVAE, which improves semantic control and generation in Transformer-based Variational AutoEncoders (VAEs). By leveraging the controllability of VQVAEs, T5VQVAE guides the self-attention mechanism in…
Continue readingThe article examines the use of Variational Autoencoders (VAEs) to create latent representations of tonal music. The authors trained the VAEs on a corpus of 371 Bach’s chorales and compared…
Continue readingThis research presents an interface based on Variational Autoencoders trained with natural sounds for the innovative creation of Foley effects. The model can transfer new sound features to prerecorded audio…
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