6 papers
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…
LiveBand: Live Accompaniment Generation in the Audio Domain
Marco Pasini, Javier Nistal, Ben Hayes +3
We present LiveBand, a real-time system that generates high-fidelity music accompaniments to live audio input, respecting strict causal constraints. Our method trains a causal tran…
Diffusion Timbre Transfer Via Mutual Information Guided Inpainting
Ching Ho Lee, Javier Nistal, Stefan Lattner +2
We study timbre transfer as an inference-time editing problem for music audio. Starting from a strong pre-trained latent diffusion model, we introduce a lightweight procedure that…
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…
Continuous Autoregressive Models with Noise Augmentation Avoid Error Accumulation
Marco Pasini, Javier Nistal, Stefan Lattner +1
Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive m…