7 citations · 23 across the 15 of their papers we have counts for
16 papers
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…
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…
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…
Towards HRTF Personalization using Denoising Diffusion Models
Juan Camilo Albarracín Sánchez, Luca Comanducci, Mirco Pezzoli +1
Head-Related Transfer Functions (HRTFs) have fundamental applications for realistic rendering in immersive audio scenarios. However, they are strongly subject-dependent as they var…
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-…
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…
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…
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…
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…
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…
Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones
Francesca Ronchini, Luca Comanducci, Mirco Pezzoli +2
Reconstructing the room transfer functions needed to calculate the complex sound field in a room has several important real-world applications. However, an unpractical number of mi…
Reconstruction of Sound Field through Diffusion Models
Federico Miotello, Luca Comanducci, Mirco Pezzoli +3
Reconstructing the sound field in a room is an important task for several applications, such as sound control and augmented (AR) or virtual reality (VR). In this paper, we propose…
Timbre transfer using image-to-image denoising diffusion implicit models
Luca Comanducci, Fabio Antonacci, Augusto Sarti
Timbre transfer techniques aim at converting the sound of a musical piece generated by one instrument into the same one as if it was played by another instrument, while maintaining…
Synthesis of Soundfields through Irregular Loudspeaker Arrays Based on Convolutional Neural Networks
Luca Comanducci, Fabio Antonacci, Augusto Sarti
Most soundfield synthesis approaches deal with extensive and regular loudspeaker arrays, which are often not suitable for home audio systems, due to physical space constraints. In…
Time Difference of Arrival Estimation from Frequency-Sliding Generalized Cross-Correlations Using Convolutional Neural Networks
Luca Comanducci, Maximo Cobos, Fabio Antonacci +1
The interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years. Time delay estimation (TDE) in adverse scenarios…
Frequency-Sliding Generalized Cross-Correlation: A Sub-band Time Delay Estimation Approach
Maximo Cobos, Fabio Antonacci, Luca Comanducci +1
The generalized cross correlation (GCC) is regarded as the most popular approach for estimating the time difference of arrival (TDOA) between the signals received at two sensors. T…