28 citations · 40 across the 11 of their papers we have counts for
7 papers · 1 filter
FolAI: Synchronized Foley Sound Generation with Semantic and Temporal Alignment
Riccardo Fosco Gramaccioni, Christian Marinoni, Emilian Postolache +4
Traditional sound design workflows rely on manual alignment of audio events to visual cues, as in Foley sound design, where everyday actions like footsteps or object interactions a…
Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
Emilian Postolache, Natalia Polouliakh, Hiroaki Kitano +4
In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroen…
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…
Generalized Multi-Source Inference for Text Conditioned Music Diffusion Models
Emilian Postolache, Giorgio Mariani, Luca Cosmo +2
Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating accompaniments, and performing source separati…
SyncFusion: Multimodal Onset-synchronized Video-to-Audio Foley Synthesis
Marco Comunità, Riccardo F. Gramaccioni, Emilian Postolache +3
Sound design involves creatively selecting, recording, and editing sound effects for various media like cinema, video games, and virtual/augmented reality. One of the most time-con…
Multi-Source Diffusion Models for Simultaneous Music Generation and Separation
Giorgio Mariani, Irene Tallini, Emilian Postolache +3
In this work, we define a diffusion-based generative model capable of both music synthesis and source separation by learning the score of the joint probability density of sources s…
Adversarial Permutation Invariant Training for Universal Sound Separation
Emilian Postolache, Jordi Pons, Santiago Pascual +1
Universal sound separation consists of separating mixes with arbitrary sounds of different types, and permutation invariant training (PIT) is used to train source agnostic models t…