Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Mirco Pezzoli"'
Autor:
Marco Olivieri, Xenofon Karakonstantis, Mirco Pezzoli, Fabio Antonacci, Augusto Sarti, Efren Fernandez-Grande
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2024, Iss 1, Pp 1-14 (2024)
Abstract Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-
Externí odkaz:
https://doaj.org/article/6005aa4b95014b97842bd29c0d30c324
Autor:
Mirco Pezzoli, Davide Perini, Alberto Bernardini, Federico Borra, Fabio Antonacci, Augusto Sarti
Publikováno v:
Sensors, Vol 22, Iss 7, p 2710 (2022)
In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural networ
Externí odkaz:
https://doaj.org/article/fb74fcf91ef54b679289ac7607c9efcf
Publikováno v:
Sensors, Vol 21, Iss 23, p 7834 (2021)
In this manuscript, we describe a novel methodology for nearfield acoustic holography (NAH). The proposed technique is based on convolutional neural networks, with autoencoder architecture, to reconstruct the pressure and velocity fields on the surfa
Externí odkaz:
https://doaj.org/article/574f677bd3a3420d9146c155dbac4b7e
Autor:
Marco Olivieri, Luca Comanducci, Mirco Pezzoli, Davide Balsarri, Luca Menescardi, Michele Buccoli, Simone Pecorino, Antonio Grosso, Fabio Antonacci, Augusto Sarti
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::94d630e3aab7cd7ac630fffe2e279edb
https://doi.org/10.1109/icassp49357.2023.10096891
https://doi.org/10.1109/icassp49357.2023.10096891
Publikováno v:
The Journal of the Acoustical Society of America. 152:354-367
The directivity pattern of a musical instrument describes the sound energy radiation as a function of frequency and direction of emission. Violins exhibit a rather complex directivity pattern, which is known to show rapid variations across frequencie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf80ce9c3ae71dd93a20532e9016feac
https://doi.org/10.1121/10.0012577
https://doi.org/10.1121/10.0012577
Publikováno v:
Sensors, Vol 21, Iss 7834, p 7834 (2021)
Sensors (Basel, Switzerland)
Sensors; Volume 21; Issue 23; Pages: 7834
Sensors (Basel, Switzerland)
Sensors; Volume 21; Issue 23; Pages: 7834
In this manuscript, we describe a novel methodology for nearfield acoustic holography (NAH). The proposed technique is based on convolutional neural networks, with autoencoder architecture, to reconstruct the pressure and velocity fields on the surfa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28a8f49e19fd30a341906e5a9a383e39
https://www.mdpi.com/1424-8220/21/23/7834
https://www.mdpi.com/1424-8220/21/23/7834
Publikováno v:
ICASSP
In the field of structural mechanics, classical methods for the vibrational characterization of objects exploit the inherent redundancy of a relevant amount of measurements acquired over regular sampling grids. However, there are cases in which parts
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c2d125bcb1443d9c879762f1e3f8faa
https://doi.org/10.1109/icassp39728.2021.9413458
https://doi.org/10.1109/icassp39728.2021.9413458
Near-field Acoustic Holography (NAH) is a well-known problem aimed at estimating the vibrational velocity field of a structure by means of acoustic measurements. In this paper, we propose a NAH technique based on Convolutional Neural Network (CNN). T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a4434e2235c47aa0dc67f414d404500
http://hdl.handle.net/11311/1192799
http://hdl.handle.net/11311/1192799
Autor:
Mirco Pezzoli, Augusto Sarti, Fabio Antonacci, Marco Olivieri, Sebastian Gonzalez, Raffaele Malvermi, Massimiliano Zanoni
Information retrieval and machine learning have grown to the point of playing a leading role in all aspects of sound analysis. There are some research areas, however, in which the potential of information retrieval techniques is only now beginning to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b2805741fcbc673ea9d31f3e01c9ad1
http://hdl.handle.net/11311/1189118
http://hdl.handle.net/11311/1189118