7 papers · 1 filter
Mitigating data replication in text-to-audio generative diffusion models through anti-memorization guidance
Francisco Messina, Francesca Ronchini, Luca Comanducci +2
A persistent challenge in generative audio models is data replication, where the model unintentionally generates parts of its training data during inference. In this work, we addre…
AI-Assisted Music Production: A User Study on Text-to-Music Models
Francesca Ronchini, Luca Comanducci, Simone Marcucci +1
Text-to-music models have revolutionized the creative landscape, offering new possibilities for music creation. Yet their integration into musicians workflows remains underexplored…
Dynamic Real-Time Ambisonics Order Adaptation for Immersive Networked Music Performances
Paolo Ostan, Carlo Centofanti, Mirco Pezzoli +3
Advanced remote applications such as Networked Music Performance (NMP) require solutions to guarantee immersive real-world-like interaction among users. Therefore, the adoption of…
VR-PTOLEMAIC: A Virtual Environment for the Perceptual Testing of Spatial Audio Algorithms
Paolo Ostan, Francesca Del Gaudio, Federico Miotello +2
The perceptual evaluation of spatial audio algorithms is an important step in the development of immersive audio applications, as it ensures that synthesized sound fields meet qual…
Diffused Responsibility: Analyzing the Energy Consumption of Generative Text-to-Audio Diffusion Models
Riccardo Passoni, Francesca Ronchini, Luca Comanducci +2
Text-to-audio models have recently emerged as a powerful technology for generating sound from textual descriptions. However, their high computational demands raise concerns about e…
Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification
Francesca Ronchini, Ho-Hsiang Wu, Wei-Cheng Lin +1
This paper investigates the design of effective prompt strategies for generating realistic datasets using Text-To-Audio (TTA) models. We also analyze different techniques for effic…