stefano squartini
Stefano Squartini (IEEE Senior Member, IEEE CIS Member, and ISCA/AES Member) was born in Ancona, Italy, on March 1976. He got the Italian Laurea with honors in electronic engineering from University of Ancona (now Polytechnic University of Marche, UnivPM), Italy, in 2002. He obtained his PhD at the same university (November 2005). He worked also as post-doctoral researcher at UnivPM from June 2006 to November 2007, when he joined the DII (Department of Information Engineering) as Assistant Professor in Circuit Theory. He is now Associate Professor at UnivPM since November 2014. His current research interests are in the area of computational intelligence and digital signal processing, with special focus on speech/audio/music processing and energy management. He is author and coauthor of many international scientific peer-reviewed articles (more than 170), and member of the Cognitive Computation, Computational Intelligence and Neuroscience, Big Data Analytics and Artificial Intelligence Reviews Editorial Boards (starting from 2011, 2014, 2015 and 2016 respectively). He is also Associate Editor of the IEEE Transactions on Cybernetics and IEEE Transactions on Emerging Topics in Computational Intelligence (2017-to date), and he was also Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems (2010-2016). He is the lead organizer of the International Workshop on Computational Energy Management in Smart Grids, at its 4th Edition in 2017 – www.cemisg.org . He is a regular reviewer for several (IEEE, Springer, Elsevier) Journals, Books and Conference Proceedings and in the recent past he organized several Special Sessions at international conferences with peer-reviewing and Special Issues of ISI journals. He joined the Organizing and the Technical Program Committees of more than 60 International Conferences and Workshops in the recent past.
Phone: 00390712204381
Address: A3lab - Department of Information Engineering
Università Politecnica delle Marche
www.a3lab.dii.univpm.it
Via Brecce Bianche 12
60131 Ancona
Italy
Phone: 00390712204381
Address: A3lab - Department of Information Engineering
Università Politecnica delle Marche
www.a3lab.dii.univpm.it
Via Brecce Bianche 12
60131 Ancona
Italy
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Papers by stefano squartini
dation in VoIP transmissions with serious impact on intelligibility
and user experience. This paper describes a system based on a
generative adversarial approach, which aims to repair the lost
fragments during the transmission of audio streams. Inspired by
the powerful image-to-image translation capability of Generative
Adversarial Networks (GANs), we propose bin2bin, an improved
pix2pix framework to achieve the translation task from magni-
tude spectrograms of audio frames with lost packets, to non-
corrupted speech spectrograms. In order to better maintain the
structural information after spectrogram translation, this paper
introduces the combination of two STFT-based loss functions,
mixed with the traditional GAN objective. Furthermore, we
employ a modified PatchGAN structure as discriminator and we
lower the concealment time by a proper initialization of the phase
reconstruction algorithm. Experimental results show that the
proposed method has obvious advantages when compared with
the current state-of-the-art methods, as it can better handle both
high packet loss rates and large gaps. We make our code publicly
available at: github.com/aircarlo/bin2bin-GAN-PLC.
This large-scale dataset is valuable for developing new driving assistance technologies based on audio or video data alone or in a multimodal manner and for improving the performance of systems currently in use. The data acquisition process with sensors in multiple locations allows for the assessment of the best installation placement concerning the task. Deep learning engineers can use this dataset to build new baselines, as a comparative benchmark, and to extend existing databases for autonomous driving.
dation in VoIP transmissions with serious impact on intelligibility
and user experience. This paper describes a system based on a
generative adversarial approach, which aims to repair the lost
fragments during the transmission of audio streams. Inspired by
the powerful image-to-image translation capability of Generative
Adversarial Networks (GANs), we propose bin2bin, an improved
pix2pix framework to achieve the translation task from magni-
tude spectrograms of audio frames with lost packets, to non-
corrupted speech spectrograms. In order to better maintain the
structural information after spectrogram translation, this paper
introduces the combination of two STFT-based loss functions,
mixed with the traditional GAN objective. Furthermore, we
employ a modified PatchGAN structure as discriminator and we
lower the concealment time by a proper initialization of the phase
reconstruction algorithm. Experimental results show that the
proposed method has obvious advantages when compared with
the current state-of-the-art methods, as it can better handle both
high packet loss rates and large gaps. We make our code publicly
available at: github.com/aircarlo/bin2bin-GAN-PLC.
This large-scale dataset is valuable for developing new driving assistance technologies based on audio or video data alone or in a multimodal manner and for improving the performance of systems currently in use. The data acquisition process with sensors in multiple locations allows for the assessment of the best installation placement concerning the task. Deep learning engineers can use this dataset to build new baselines, as a comparative benchmark, and to extend existing databases for autonomous driving.