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20192025
most citedTowards HRTF Personalization using Denoising Diffusion Models

7 citations · 23 across the 15 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

eess.AS2024★ 1 cited

FakeMusicCaps: a Dataset for Detection and Attribution of Synthetic Music Generated via Text-to-Music Models

Luca Comanducci, Paolo Bestagini, Stefano Tubaro

Text-To-Music (TTM) models have recently revolutionized the automatic music generation research field. Specifically, by reaching superior performances to all previous state-of-the-…

eess.AS2024★ 4 cited

MambaFoley: Foley Sound Generation using Selective State-Space Models

Marco Furio Colombo, Francesca Ronchini, Luca Comanducci +1

Recent advancements in deep learning have led to widespread use of techniques for audio content generation, notably employing Denoising Diffusion Probabilistic Models (DDPM) across…

cs.SD2024★ 6 cited

PAGURI: a user experience study of creative interaction with text-to-music models

Francesca Ronchini, Luca Comanducci, Gabriele Perego +1

In recent years, text-to-music models have been the biggest breakthrough in automatic music generation. While they are unquestionably a showcase of technological progress, it is no…

eess.AS2024

Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance Propagation

Luca Comanducci, Fabio Antonacci, Augusto Sarti

Deep learning models are widely applied in the signal processing community, yet their inner working procedure is often treated as a black box. In this paper, we investigate the use…

eess.AS2024

Synthetic training set generation using text-to-audio models for environmental sound classification

Francesca Ronchini, Luca Comanducci, Fabio Antonacci

In recent years, text-to-audio models have revolutionized the field of automatic audio generation. This paper investigates their application in generating synthetic datasets for tr…

eess.AS2024

HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays

Federico Miotello, Paolo Ostan, Mirco Pezzoli +4

In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to m…