65 citations · 183 across the 27 of their papers we have counts for
18 papers · 1 filter
Physics-Informed Transfer Learning for Data-Driven Sound Source Reconstruction in Near-Field Acoustic Holography
Xinmeng Luan, Mirco Pezzoli, Fabio Antonacci +1
We propose a transfer learning framework for sound source reconstruction in Near-field Acoustic Holography (NAH), which adapts a well-trained data-driven model from one type of sou…
Low-Rank Adaptation of Deep Prior Neural Networks For Room Impulse Response Reconstruction
Mirco Pezzoli, Federico Miotello, Shoichi Koyama +1
The Deep Prior framework has emerged as a powerful generative tool which can be used for reconstructing sound fields in an environment from few sparse pressure measurements. It emp…
Physics-Informed Neural Network-Driven Sparse Field Discretization Method for Near-Field Acoustic Holography
Xinmeng Luan, Mirco Pezzoli, Fabio Antonacci +1
We propose the Physics-Informed Neural Network-driven Sparse Field Discretization method (PINN-SFD), a novel self-supervised, physics-informed deep learning approach for addressing…
Past, Present, and Future of Spatial Audio and Room Acoustics
Shoichi Koyama, Enzo De Sena, Prasanga Samarasinghe +2
The study of spatial audio and room acoustics aims to create immersive audio experiences by modeling the physics and psychoacoustics of how sound behaves in space. In the long hist…
A Zero-Shot Physics-Informed Dictionary Learning Approach for Sound Field Reconstruction
Stefano Damiano, Federico Miotello, Mirco Pezzoli +4
Sound field reconstruction aims to estimate pressure fields in areas lacking direct measurements. Existing techniques often rely on strong assumptions or face challenges related to…
A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays
Federico Miotello, Ferdinando Terminiello, Mirco Pezzoli +3
Spherical microphone arrays are convenient tools for capturing the spatial characteristics of a sound field. However, achieving superior spatial resolution requires arrays with num…
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…
Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals
Marco Olivieri, Xenofon Karakonstantis, Mirco Pezzoli +3
Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based app…
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…
Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responses
Mirco Pezzoli, Fabio Antonacci, Augusto Sarti
Recently deep learning and machine learning approaches have been widely employed for various applications in acoustics. Nonetheless, in the area of sound field processing and recon…
Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations
Maximo Cobos, Mirco Pezzoli, Fabio Antonacci +1
Acoustic signal processing in the spherical harmonics domain (SHD) is an active research area that exploits the signals acquired by higher order microphone arrays. A very important…
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…