2 citations · 2 across the 11 of their papers we have counts for
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Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders
Mathias Rose Bjare, Giorgia Cantisani, Marco Pasini +2
We argue that training autoencoders to reconstruct inputs from noised versions of their encodings, when combined with perceptually motivated losses, yields encodings that are struc…
CoDiCodec: Unifying Continuous and Discrete Compressed Representations of Audio
Marco Pasini, Stefan Lattner, George Fazekas
Efficiently representing audio signals in a compressed latent space is critical for latent generative modelling. However, existing autoencoders often force a choice between continu…
Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding
Marco Pasini, Stefan Lattner, George Fazekas
Efficiently compressing high-dimensional audio signals into a compact and informative latent space is crucial for various tasks, including generative modeling and music information…