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Giorgio Mariani

4 accepted papers

2025

COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations

Ruben Ciranni, Giorgio Mariani, Michele Mancusi, Emilian Postolache, Giorgio Fabbro, Emanuele Rodolà +1
ICASSP 2025accepted

We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coherence between samples. Our method operates at the level of the individual stems composing music tracks and can input feat…

Cited by 0Source ↗Scholar ↗
2024

Generalized Multi-Source Inference for Text Conditioned Music Diffusion Models

Emilian Postolache, Giorgio Mariani, Luca Cosmo, Emmanouil Benetos, Emanuele Rodolà
ICASSP 2024accepted

Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating accompaniments, and performing source separation. Despite their versatility, they require estimating the joint distribution over the sources, necessitating pre-separa…

Cited by 0Source ↗Scholar ↗
2024

Multi-Source Diffusion Models for Simultaneous Music Generation and Separation

Giorgio Mariani, Irene Tallini, Emilian Postolache, Michele Mancusi, Luca Cosmo, Emanuele Rodolà
ICLR 2024oral

In this work, we define a diffusion-based generative model capable of both music generation and source separation by learning the score of the joint probability density of sources sharing a context. Alongside the classic total inference tasks (i.e., generating a mixture, separating the sources), we…

Cited by 46Source ↗PDF ↗code ↗Scholar ↗
2023

Latent Autoregressive Source Separation

Emilian Postolache, Giorgio Mariani, Michele Mancusi, Andrea Santilli, Luca Cosmo, Emanuele Rodolà
AAAI 2023technical

Autoregressive models have achieved impressive results over a wide range of domains in terms of generation quality and downstream task performance. In the continuous domain, a key factor behind this success is the usage of quantized latent spaces (e.g., obtained via VQ-VAE autoencoders), which allow…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗