activity
20222025
most citedFirst Results on the Search for Lepton Number Violating Neutrinoless Double Beta Decay with the LEGEND-200 Experiment

19 citations · 38 across the 10 of their papers we have counts for

collaborators
Showing cs.SDShow all

7 papers · 1 filter

cs.SD2025

Virtual Consistency for Audio Editing

Matthieu Cervera, Francesco Paissan, Mirco Ravanelli +1

Free-form, text-based audio editing remains a persistent challenge, despite progress in inversion-based neural methods. Current approaches rely on slow inversion procedures, limiti…

cs.SD2024

Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech

Eleonora Mancini, Francesco Paissan, Paolo Torroni +2

Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detection have shown strong performance, their i…

cs.SD2024

LMAC-TD: Producing Time Domain Explanations for Audio Classifiers

Eleonora Mancini, Francesco Paissan, Mirco Ravanelli +1

Neural networks are typically black-boxes that remain opaque with regards to their decision mechanisms. Several works in the literature have proposed post-hoc explanation methods t…

cs.SD2024★ 1 cited

Listenable Maps for Zero-Shot Audio Classifiers

Francesco Paissan, Luca Della Libera, Mirco Ravanelli +1

Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthiness of this technology. In this paper, we…

cs.SD2024

Listenable Maps for Audio Classifiers

Francesco Paissan, Mirco Ravanelli, Cem Subakan

Despite the impressive performance of deep learning models across diverse tasks, their complexity poses challenges for interpretation. This challenge is particularly evident for au…

cs.SD2023★ 5 cited

Audio Editing with Non-Rigid Text Prompts

Francesco Paissan, Luca Della Libera, Zhepei Wang +3

In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits that remain faithful to the input audio.…

cs.SD2023★ 2 cited

Scaling strategies for on-device low-complexity source separation with Conv-Tasnet

Mohamed Nabih Ali, Francesco Paissan, Daniele Falavigna +1

Recently, several very effective neural approaches for single-channel speech separation have been presented in the literature. However, due to the size and complexity of these mode…