28 citations · 41 across the 10 of their papers we have counts for
11 papers
SAGE: Semantic Audio Generative Encoder
Francesco Brigante, Luca Cerovaz, Davide Marincione +4
Audio autoencoders compress waveforms into compact latent representations that serve as the interface between raw audio and downstream models. Current systems navigate a three-way…
How Neural Losses Shape VAE Latents
Giorgio Strano, Luca Cerovaz, Michele Mancusi +2
Modern VAEs are rarely trained with the pointwise likelihood implied by the standard -VAE objective. In practice, pointwise reconstruction is often combined with perceptual and…
EuleroDec: A Complex-Valued RVQ-VAE for Efficient and Robust Audio Coding
Luca Cerovaz, Michele Mancusi, Emanuele Rodolà
Audio codecs power discrete music generative modelling, music streaming and immersive media by shrinking PCM audio to bandwidth-friendly bit-rates. Recent works have gravitated tow…
STAGE: Stemmed Accompaniment Generation through Prefix-Based Conditioning
Giorgio Strano, Chiara Ballanti, Donato Crisostomi +3
Recent advances in generative models have made it possible to create high-quality, coherent music, with some systems delivering production-level output. Yet, most existing models f…
COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
Ruben Ciranni, Giorgio Mariani, Michele Mancusi +4
We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coher…
Accelerating Transformer Inference for Translation via Parallel Decoding
Andrea Santilli, Silvio Severino, Emilian Postolache +4
Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solv…