Thesis type
3D audioPezzoliPhysics-informed deep prior for sound field reconstructionFull thesisThe estimation of sound field provided by neural networks such has deep prior, can potentially diverge from the underlying physics. This thesis aims at defining novel paradigm for sound field reconstruciton that leverage on the generational power of neural networks and prior knowledge of physics. [Required knowledge: deep learning, acoustics] [Thesis in collaboration with Prof. S. Koyama of the National Institute of Informatics - Tokyo.]
3D audioPezzoliRay-space-based kernel interpolationFull thesisSound field reconstruction is at the base of several spatial audio applications. In this thesis the combination of sound field representations and kernel interpolation enables to overcome current limitations of sound field reconstruction with potential benefits in severl applications. [Required knowledge: acoustics] [Thesis in collaboration with Prof. S. Koyama of the National Institute of Informatics - Tokyo.]
3D audioPezzoli Physics-informed deep kernel interpolation for sound field reconstructionFull thesisSound field reconstruction is at the base of several spatial audio applications. Deep kernel learning has potential application for the reconstruction of the sound field thanks to the possibility of adopting physics-informed neural network in order to impose prior knowledge of the acoustics. The thesis aims to develop novel deep kernel models for the reconstruction of acoustic fields. [Required knowledge: deep learning, acoustics] [Thesis in collaboration with Prof. S. Koyama of the National Institute of Informatics - Tokyo.]
Audio Signal Processing
BernardiniStrategies for Clipping Prevention in Dynamic Sound FilteringFull thesis The thesis aims to validate a method capable of predicting the occurrence of clipping at the output of a network of parametric digital filters, typically used in digital audio effects. If validated, this method would enable us to continuously monitor the values that a filtering parameter can assume without causing clipping. The student will assess the effectiveness of the method, particularly in parametric equalizers, highlighting aspects of robustness and weaknesses in specific implementations when their parameters are altered during equalization.
Audio Signal Processing
Mezza, BernardiniDeep Packet Loss Concealment for Speech and MusicFull thesisAudio communications over the Internet have become an integral part of everyday life. However, speed is often prioritized over reliability in order to respect strict real-time constraints. Consequently, short audio segments (packets) risk being severely delayed or lost. We recently developed deep Packet Loss Concealment (PLC) methods, as well as hybrid PLC algorithms combining signal-processing and deep-learning techniques in a synergistic way. In this thesis, we will explore its performance on speech and/or music signals. The thesis won't deal with network-related and other IP-related aspects. [Required knowledge: signal processing theory; practical experience with deep learning.]
Audio Signal Processing
Mezza, Giampiccolo, BernardiniMusic DemixingFull thesisMusic Demixing (MDX) refers to a set of novel techniques aimed at separating and extracting the audio stems instruments that makes up a given song. Think of Spleeter by Deezer, Meta's Demucs, or the Ultimate Vocal Remover. The MDX field is very much growing in the past few years, but the problem is long from being solved. In this thesis, we will develop cutting-edge deep-learning models for demixing music signals and drums recordings. [Required knowledge: experience with deep learning tools and libraries]
3D audioOlivieri, Pezzoli, AntonacciNearfield filter for spherical microphone array recordingsFull thesisSpherical Microphone Arrays (SMA) are very suitable for binaural rendering and in general for spatial audio applications. In this thesis we are interested in developing new methods to filter the undesired signals in a nearfield region of the SMA.
Audio Signal Processing
Olivieri, Pezzoli, AntonacciDevelopment of acoustic simulation framework for GPUShort thesisParallel implementations speed up the computation of acoustic simulations. In this short thesis it is required to develop a Room Impulse Response renderer for Spherical Microphone Arrays for GPUs. The software will be preferrabily developed with CUDA and Python. Required knowledge: computational acoustics (RIRs, Image Source Method, ...) and experience with practical coding.
Musical AcousticsPezzoli, Cillo, LongoEnhancement of a reduced-order finite-element model of a classical guitarFull/short thesis[Thesis abroad at the Institute of Engineering and Computational Mechanics (ITM), University of Stuttgart, Germany.]
A recently developed high-fidelity finite-element guitar model combined with experimental modal analysis can successfully identify the material characteristics of already existing instruments.
Parametric Model Order Reduction (PMOR) is applied to significantly reduce the computational time of the model. During the PMOR procedure, minor simplifications to the model need to be undertaken, leading to deviations of the reduced-order model from the original model.
This thesis aims to enhance the reduced-order model via optimization and/or data-driven methods to compensate for the error term resulting from the simplifications in the reduced-order model.
Required knowledge: foundations on Finite Element Methods, in depth-knowledge of deep learning (long version).
Audio forensicsBestagini, Antonacci
Detection of text-to-speech algorithmsFull thesisNowadays, text-to-speech and voice conversion algorithms are able to produce very realistic speech signals, which can easily trick human ear. Moreover, this technology is in rapid evolution and it is not possible to take in account all new synthesis methods. It is necessary to develop effective synthetic speech detection systems able to work in open-set scenarios.
Audio signal processing
Antonacci, PezzoliDeep learning solution for localization of acoustic sources in the spherical harmonics domainFull thesisThe spherical harmonics representation of the sound field is a widely adopted description of spatial sound. The goal of this thesis is to devise deep learning solutions that exploit the spherical harmonics representation for the analysis of the acoustic field e.g., localization of acoustic sources. [Required knowledge: spherical harmonics decomposition of sound field, theoretical knowledge and pratical experience with deep learning]
Audio signal processing
Giampiccolo, BernardiniVirtual Analog Modeling, Audio Circuit Emulation, Physical Modeling Sound Synthesis through Wave Digital FiltersFull thesis
Differential Microphone ArraysBernardini, AlbertiniTwo-Stage Differential Beamforming over Networks of Microphone ArraysFull thesis[Intership at ST Microelectronics, Agrate Brianza (BG)] Differential Microphone Arrays (DMAs) have attracted significant attention in the field of acoustic array processing due to their frequency-invariant spatial responses and small size. Recently, there has been a growing interest in systems combining the output of "local" DMA units to perform further spatial filtering. Up to now, systems composed of multiple DMA units are confined to a spatially localized area. By using a more complex sound source propagation model, the thesis will explore the development of spatial filters (beamformers) relying on multiple spatially distributed DMAs.​ The thesis will involve both theoretical and more implementative aspects.
Musical AcousticsAntonacci, Olivieri, PezzoliTransfer Learning techniques for Nearfield Acoustic Holography analysisFull/short thesisRecent data-driven based NAH methods can predict the vibrational behavior on sources from the acquisition of the radiated sound field. Nevertheless, these approaches are dependent on the training dataset used (i.e., acquisition setup and vibrational source). This thesis aims at extending the recent solutions with transfer learning strategies in order to tune the networks with different data and improving the model with specific physical priors to reconstruct the vibrational content with an unsupervised approach (long thesis). [knowledge of Deep Learning required]
Musical AcousticsGonzalez, Malvermi, AntonacciExperimental measurement and construction of violin top platesFull/short thesisThe aim of this thesis is twofold: measure the material properties of violin top plates and build violin top plates with certain material properties. For this the student will use a CNC router to build the plates and a experimental set up that measures the FRF of the plate to compute its material parameters. The goal is to be able to produce top plates with a defined mechanical response irrespective of the varying material parameters of the wood the top is made of. Experimental thesis in Cremona Campus, FEM modelling required, Fusion 360 optional.
Musical AcousticsGonzalez, AntonacciRole of f-hole design in stress distribution on the violin top plateLong thesisThe role of the f-holes in violins is to let the sound vibrations leave the body of the instrument and reach the audience. However, cutting holes in the top plate weakens it. By cutting curves and circles, the instrument maker avoids creating the stress concentrations associated with sharp corners. The aim of this thesis is to study the stress behaviour in a violin soundboard for different f-hole designs. Comsol experience preferred.
Musical AcousticsGonzalez, Malvermi, AntonacciEffect of tailpiece height in the acoustic response of a violinLong thesisVarying the height of the tailpiece is one of the ways luthiers can control the sound production of the violin. By changing the angle of the strings, there is a modification in the effective pressure that the bridge, and consequently the violin top plate, feels. This compression of the violin is believed to affect the sound production of the instrument. This thesis aims to study, by means of simulations, the effect the net static force in the bridge has in the dynamics of the instrument. If time allows the thesis could also include experimental measurements with the help of Amorim fine violins.
Musical AcousticsGonzalez, Longo, AntonacciLinear interpolation between shapes in western guitarsLong thesisIn one of our last thesis projects we have developed a completely parametric model of the guitar. The objective of this thesis is to study how vibrational characteristics change when smoothly varying the shape of a guitar between standard models, say between a Jumbo and a Dreadnought. The work involves the creation of different virtual models and its study with Comsol multiphysics.
Musical AcousticsGonzalez, Greco, AntonacciTimbral Study of 3D printed organ pipesLong thesisRecently, researchers have presented a theoretical model to understand the timbre of the organ by mapping its sound to a bi-dimensional map in the spectral-centroid and envelope slope of the spectra. This thesis wants to study how geometric variations in 3D printed organ pipes determine the location of the sound in this timbral map.
Musical AcousticsGonzalez, Malvermi, AntonacciExperimental study of wooden metamaterialsLong thesisExperimental realisation of metamaterials for instrument making: guitar top plates, violin top plates, archtop top plates. Studies of vibrational and stiffness behaviour. Needs to live in Cremona.
Musical AcousticsGonzalez, AntonacciDeveloping a new Manouche guitar: studying different bracings models for the gypsy jazz icon Long thesisManouche guitars are a mix between mandolins, parlour and archtop guitars. Created in Paris by Italian luthier Macaferri, they represent a particular understanding of how to make instruments. Their design takes from the parlour guitar in terms of bracing, from the archtop in its shape and floating bridge, and from the mandolin in its bent top plate. The aim of this thesis is to study, by means of simulations, different bracing patterns that could inform a new way of crafting these instruments. The selected model when then be built by one of the advisors.
Musical AcousticsGreco, AntonacciNeural Network-Based Prediction of Woodwind Mouthpiece Sound Characteristics through Finite Element Method SimulationsLong thesisThis master's thesis proposes a novel approach to explore the relationship between geometric parameters of woodwind instrument mouthpieces and their corresponding sound characteristics. Employing COMSOL Multiphysics, Finite Element Method (FEM) simulations will be conducted to assess impedance variations. Simulated geometries will be transformed into transfer matrices to create a dataset for training a neural network. The objective is to develop a predictive model capable of estimating sound behavior without explicit FEM simulations, thus offering a more efficient and accessible method for instrument design and optimization. The study aims to contribute to the field of music and acoustic engineering by reducing computational costs and time associated with traditional simulation methods.
Generative AI for musicRonchini, ComanducciEnhancing understanding and transparency in Text-to-Music generative models through eXplainable Artificial IntelligenceLong thesisThe field of text-to-music generative models is currently experiencing a surge in research activity, indicative of the growing enthusiasm for artificial intelligence in creative domains. Despite this fervor, recent investigations have uncovered critical deficiencies in existing text-to-audio and text-to-music generative models, but their black-box structure makes it difficult to understand the reasons behind their generation and predictions. The goal of this thesis is to achieve a profound understanding of the underlying mechanisms through the incorporation of eXplainable Artificial Intelligence (XAI) techniques in text-to-music/audio generative models. This integration of XAI techniques aims to provide transparency and interpretability in the functioning of the generative model, facilitating a deeper comprehension of its inner workings and fostering advancements in the broader field of creative artificial intelligence applications
Deep Learning for audioRonchini, ComanducciBalance between performance end carbon footspring of state-of-the-art deep learning systems for audio domain applicationsLong thesisThe growth of deep learning systems has brought forth a notable concern: a trajectory toward increasing complexity and energy consumption. The goal of the project is to understand what is the carbon footprint of state-of-the-art deep learning models in the content of text-to-music models or sound event detection, classification, and localization models (based on the interest of the student). The thesis will focus on the examination of different impactful parameter of the model(s) trying to understand which is the environmental impact of the different systems, identifying the most critical aspects of systems during both training and inference phases.
Large Language Models for AudioRonchini, ComanducciLanguage-Queried Audio Source SeparationLong thesisThe objective of Language-based audio source separation (LASS) is to separate diverse sounds by employing textual descriptions of the desired output. In the field of source separation, LASS is a potent tool, empowering users to isolate audio sources through natural language commands. The project aims to explore deep learning techniques to effectively separate sound sources by utilizing natural language queries, driving progress in how we interact with and manipulate audio content.
Generative AI for audioRonchini, ComanducciFoley sound synthesis/Sound Scene SynthesisLong thesisFoley sounds, also known as sound effects integrated into multimedia during post-production, serve to enhance its perceived acoustic properties. In the last years, there has been an increasing interest in using AI tools for sound synthesis and generative models to assist the generation of Foley sounds. The scope of this thesis is to explore different approaches for the generation of original audio clips of various sound categories. The thesis will start with the implementation of a Foley sound generation baseline, which th student will improve with deep learning techniques.
Sound Event Detection and LocalizationRonchini, ComanducciAudio and Audiovisual Sound Event Localization and Detection with Source Distance EstimationLong thesisThe goal of Sound Event Localization and Detection (SELD) refers to the combined task of Sound Event Detection (SED) and Sound Event Localization (SEL), whose aim is the recognition of sound sources and their spatial location. The applications of this task are multiple, such as smart-home applications, scene visualization systems, and acoustic monitoring, among others. In recent years, multi-modal deep learning techniques have been applied to the SELD tasks, also including visual cues in the training dataset. The goal of the thesis is to understand how to include and exploit visual and audio information together, with the goal of improving the SELD task using multi-modal deep learning techniques.
Generative AI for audioRonchini, ComanducciExploring Text-to-Audio Models for Audio Data Training in System DevelopmentLong thesisThis thesis aims to investigate the efficacy of using text-to-audio models for generating audio data that can subsequently be employed to train deep learning systems for different applications such sound event detection, acoustic scene classification, etc. Text-to-audio models offer the capability to convert textual inputs into high-fidelity audio representations, but some field of applications have not been completely addresses yet. How can the audio data generated by text-to-audio models be effectively integrated into system training pipelines? What are the potential challenges and limitations associated with utilizing text-to-audio generated data for system training, and how can these challenges be addressed? The goal of the project is to develop methodologies for generating diverse audio datasets using text-to-audio models and investigate the applicability of text-to-audio generated data in training various systems, identifying potential use cases and applications.
Image and Video
Thesis type
Image/video forensicsBestagini, Mandelli, Cannas
Detect and localize image and video manipulations
FullImages and videos can be manipulated in many different ways (e.g., object insertion and removal, local retouching, laundering operations, etc.). We are interested in developing methods to detect and localize possible editing operations on images and videos.
Image/video forensicsBestagini, Mandelli, Cannas
Distinguish original videos from DeepFakesFullDeepFake videos can be maliciously spread online. We are interested in developing techniques to detect whether a video is a DeepFake or not, why a detector says a video is fake, and understand which DeepFake generation software has been used to create a video.
Image/video forensicsBestagini, Mandelli, Cannas
Assess the authenticity of satellite imagesFullSatellites can acquire visual data with different sensors. We are interested in developing techniques that verify whether an overhead image has been edited or not.
Image/video forensicsBestagini, Mandelli, Cannas
Forensic analysis of scientific imagesFullScientific publications in the life science area typically contain charcateristic kinds of images to showcase the achieved results (e.g., western blots, microscopy acquisitions, etc.). As these images differ from natural photographs, we are interested in developing novel techniques to detect possible scientific image forgery operations.
Image processingBestagini, Mandelli, GigantiEnhanceent of emission mapsFullBiogenic Volatile Organic Compounds (BVOCs) are gases emitted by plants under different leveles of stress. The study of these emissions is paramount for several applications related to environmental and pollution control. Measuring these emissions is often very challenging. It is therefore customary to only have a few sparse measurements over the area to control. The goal of this work is to apply interpolation and super-resolution techniques to fill-in the gaps in sparse emission maps.
Thesis type
Tubaro, LipariImproving Full Waveform Inversion with CNNsFull/shortFull Waveform Inversion reconstructs the subsurface velocities from a set of measurements. It is very expensive, time-consuming and prone to a number of tips and tricks for avoiding local minima, numerical instability and optimization errors.
Tubaro, LipariDenoising and Interpolation of seismic data through CNNsFull/shortThe amount of data is constantly increasing and the areas of interest are more and more complex to analyze. Moreover, they require a subsurface mapping at increasingly higher resolution and higher fidelity. Can CNNs help this process?
Tubaro, LipariMachine Learning guided Seismic InterpretationFull/shortHuman experts visually inspects seismic images looking for subsurface features. On the other hand, Machine Learning techniques have proven to be effective in image segmentation (i.e., recognizing objects and targets from a set of pixels). Can we merge these two worlds?
Tubaro, LipariPhysics-aware Transfer LearningFull/shortCan a neural network learn Physics?
Tubaro, LipariRegularizing Traveltime Tomography via Machine LearningFull/shortTraveltime Tomography is an important tool for localizing subsurface events, both in medical and geophysical imaging. The tomography problem is a ill-posed and ill-conditioned inverse problem, which suffers a lot from numberical instability, local minima, and noise. Here's where the machine learning comes into play!

Currently on-going

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Musical acousticsPezzoli, MalvermiStatiscal charcterization of directivity Gian Marco Ricci
Musical acousticsPezzoli, MalvermiDeep prior based vibroacustic analysisRiccardo Sebastiani Croce
Musical acousticsPezzoli, MalvermiPINN based vibroacoustic analysisFederico Zese
3D audioPezzoli, GrecoLocalization of sound sources using spherical harmonicsSilvia Messena
Musical acousticsOlivieri, PezzoliNearfield Acoustic Holography solver based on Physics-Informed Neural NetworkXinmeng Luan
Audio signal processing
Giampiccolo, Bernardini2D Canonical Piecewise-Linear functions for the Wave Digital Modeling of 2-port NonlinearitiesValerio Maiolo
3D audioPezzoliAcoustic Virtual Reality evaluation systemFrancesca Del Gaudio
Audio signal processingMassi, Giampiccolo, BernardiniDeep Learning Models of Nonlinear Time-Varying Circuits in the Wave Digital DomainShijie Yang
Audio signal processingGiampiccolo, BernardiniAutomatic Generation of VSTs based on WDFsStefano Ravasi
Audio signal processingGiampiccolo, BernardiniModeling Circuits with Two Multiport NonlinearitiesSebastian Gafencu
Audio signal processingGiampiccolo, BernardiniModeling of MOSFETs for Virtual Analog ApplicationsMarco Ferrè
3D audioPezzoli, MalvermiNeural Network-based representation of sound source directivityEdoardo Morena
Audio signal processing
Massi, Giampiccolo, BernardiniOptimization of MEMs Loudspeaker circuital models via Automatic DifferentiationLelio Casale
Space-time audioPezzoli, MiotelloSpherical microphone array upsamplingFerdinando Terminiello
Space-time audioPezzoli, GrecoSound field reconstruction for 6DoF navigationSilvio Attolini
Space-time audioPezzoli, MiotelloReal-time microphone array rendering framework for binaural reproductionPaolo Ostan
Space-time audioAntonacci, PezzoliSound field separation in the spherical harmonics domain Sagi Della-Torre
Audio signal processing
Giampiccolo, Massi, BernardiniVacuum Tubes Modeling by means of Neural Networks in the Wave Digital DomainGenis Casanova
Audio signal processingGiampiccolo, BernardiniModeling of Nonlinear Piezoelectric LoudspeakersArmando Boemio
Image forensicsBestagini, MandelliManipulation detection for scientific imagesGiovanni Zanocco
Music informaticsSarti, Mezza, BernardiniUnsupervised selection of harmonic complexity metricsGiorgio De Luca
Music informaticsZanoni, BorrelliSocial interaction based music recommendation systemCarlo Pulvirenti
Music informaticsSarti, BorrelliConnecting NN to bio-metric signalsJoep Rene Wulms
Musical AcousticsGonzalez, AntonacciRandom variation of guitar bracingsMattia Vanessa
Musical acousticsGonzalez, AntonacciMetamaterials for guitarmakingGabriele Marelli, Mattia Lercari
Musical acoustics / AIGonzalez, AntonacciAI-powered pick up: making guitars sound great againEmanuele Voltini
Space-time audioPezzoli, ComanducciGenerative Models for HRTF predictionJuan Camilo Albarracín Sánchez
Music InformaticsSarti, ComanducciHandMonizer, personalized digital musical instrument designAntonios Pappas
Music InformaticsComanducci, MezzaImpact of velocity on drum patterns perceived complexityGabriele Maucione
Generative AI for audioComanducci, RonchiniAdding temporal information and event order modeling to generative models for audio/musicMarco Furio Colombo
Deep Learning for audioRonchini, ComanducciBalance between performance end carbon footspring of state-of-the-art deep learning systems for audio domain applicationsRiccardo Passoni

Past (from 2017)

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Music InformaticsComanducci, Ronchini, ZanoniPersonalized Music Generation using text-to-music modelsGabriele Perego
Space-time audioPezzoliAnalysis of the directivity of sound sourcesHou Hin Au-Yeung
Audio signal processingBernardini, Giampiccolo, AlbertiniApplication of antiderivative antialiasing to MOSFET elements in wave digital filtersChristian Parra
Music InformaticsZanoni, ComanducciProcedural Music Generation For Video gamesFrancesco Zumerle
Audio signal processingBernardini, Giampiccolo, MezzaOn the Use of Fundamental Frequency Estimation for Virtual Bass EnhancementFabio Spreafico
Video forensicsBestagini, CannasDeepfake video detection through multi-look analysisAdriano Bonfantini
Video processingBestagini, RedondiAutomatic video analysis of badminton matchesIvan Motasov
Space-time audioBernardini, Giampiccolo, MezzaDesigning of Scattering Delay Networks Via Automatic DifferentiationFrancesco Boarino
Audio signal processingBernardini, GiampiccoloA Wave Digital Extended Fixed-Point Method for Virtual Analog ApplicationsDavide Marin Pasin
Musical AcousticsRipamonti, Malvermi, GonzalezExperimental Validation for data-driven Near-field Acoustic HolographyAlessio Lampis
Musical AcousticsAntonacci, MalvermiImproved sensors for low-cost Vibrometric KitFabio Guarnieri
Audio signal processingBernardini, GiampiccoloA Wave Digital Hierarchical Quasi-Newton Method for Virtual Analog ModelingLuca Gobbato
Musical AcousticsSarti, Paoletti, Adali, MalvermiAcoustic Characterization of materialsMarco Donzelli
Music Informatics Zanoni, ComanducciDeep Learning-based Timbre TransferSilvio Pol
Audio signal processingAntonacci, Pezzoli, BorraA perceptual evaluation of sound field reconstruction algorithmsMiriam Papagno
Audio signal processing
Bernardini, GiampiccoloCharacterization of Small-Size Loudspeakers for Mobile ApplicationsSamuele Buonassisi
Image forensicsBestagini, CannasEnhanced Amplitude SAR Imagery Splicing Localization through Land Cover Mapping TechniquesEmanuele Intagliata
GeophysicsBestagini, LipariSalt Segmentation of Geophysical Images through Explainable CNNsFrancesco Maffezzoli
Audio forensicsBestagini, BorrelliA metric learning approach for splicing localization based on synthetic speech detectionFrancesco Castelli
Audio forensicsBestagini, BorrelliCombining automatic speaker verification and prosody analysis for synthetic speech detectionLuigi Attorresi
Music informaticsBestagini, CuccovilloSpeech fingerprinting and matching for content retrievalLaura Colzani
Musical AcousticsAntonacci, OlivieriTowards white-box data-driven methods for Near-field Acoustic HolographyHagar Kafri
Video forensicsBestaginiA CNN-based detector for video frame-rate interpolationSimone Mariani
Image/video processingBestaginiAudio-video techniques for the analysis of players behaviour in Badminton matchesSamuele Bosi
Video forensicsBestagini, MandelliForensic detection of deepfakes generated through video-to-video translationCarmelo Fascella
Audio signal processing
Bernardini, Mezza, GiampiccoloWave Digital Filter Modeling of Audio Circuits with Hysteresis Nonlinearities using Neural NetworksOliviero Massi
Music informaticsAntonacci, Pezzoli, ComanducciDeep Prior Audio InpaintingFederico Miotello
Audio signal processingBestagini, BuccoliLow-latency speaker recognitionFrancesco Salani
Video forensicsBestagini, BonettiniA Data Driven Approach to Deepfake Detection via Feature Analysis Based on Limited Data Bingyang Hu
Space-time audioAntonacci, Borrelli, BorraBeamforming and Speaker Identification through Deep Neural Networks Matteo Scerbo
Music informaticsSarti, BorrelliHarmonic complexity estimation of jazz musicGiovanni Agosti
Audio forensicsAntonacci, BorrelliA model selection method for room shape classification based on mono speech signalsGabriele Antonacci
Audio forensicsBestaginiAudio splicing detection and localization based on recording device cuesDaniele Ugo Leonzio
Audio forensicsBestaginiSpeaker-Independent Microphone Identification via Blind Channel Estimation in Noisy ConditionAntonio Giganti
Audio forensicsBestagini, BorrelliSynthetic Speech Detection through Convolutional Neural Networks in Noisy EnvironmentsEleonora Landini
Audio forensicsBestagini, Borrelli, SalviSynthetic speech detection based on sentiment analysisEmanuele Conti
Multimedia forensicsBestagini, Salvi, BorrelliAudio-video deepfake detection through emotion recognitionJacopo Gino
Audio signal processingSarti, Giampiccolo, BernardiniParallel Wave Digital Implementations of Nonlinear Audio CircuitsNatoli Antonino
Musical AcousticsAntonacci, MalvermiData driven methods for frequency response functions interpolationMatteo Acerbi
Audio forensicsBestagini, MandelliTime-Scaling Detection in Audio RecordingsMichele Pilia
Audio forensicsBestagini, BorrelliSpeech Intelligibility Parameters Estimation Through Convolutional Neural NetworksMattia Papa
Audio forensicsAntonacciClosed and open set classification of real and AI synthesised speechMichelangelo Medori
Audio forensicsAntonacciAn approach to room volume estimation from single-channel speech signals based on neural networksCastelnuovo Carlo
Audio forensicsBestaginiAudio Splicing Detection and Localization Based on Acoustic CuesCapoferri Davide
Audio processingSarti, ComanducciAudio frame reconstruction from incomplete observations using Deep Learning techniquesSchils Minh Cédric
Audio processingSarti, BernardiniWave Digital Modeling and Simulation of Nonlinear Electromagnetic CircuitsGiampiccolo Riccardo
Audio processingSarti, BernardiniAntiderivative Antialiasing in Nonlinear Wave Digital FiltersAlbertini Davide
Audio processingSarti, BernardiniWave Digital Implementation of Nonlinear Audio Circuits based on the Scattering Iterative MethodProverbio Alessandro
Audio processingAntonacciA system for super resolution vibrometric analysis through convolutional neural networksCampagnoli Chiara
Audio processingAntonacciDevelopment of a low-cost platform for acoustic and vibrometric analysis on lutherie products, with a special focus on the estimation of the elastic parameters of the tonewoodVilla Luca
Audio processingBestaginiDNN based post-filtering for quality improvement of AMR-WB decoded speechGupta Kishan
Audio processingSartiStudio sull'implementazione degli algoritmi per il musical instruments ed il sound reinforcement basato su un processore multicoreAretino Michele
Audio processingSarti, BernardiniModeling nonlinear 3-terminal devices in the wave digital domainVergani Alessio Emanuele
ForensicsBestaginiConvolutional and recurrent neural networks for video tampering detection and localizationCannas Edoardo Daniele
ForensicsBestaginiA study on Bagging-Voronoi algorithm for tampering localizationCereghetti Corinne Elena
ForensicsBestaginiJPEG-based forensics through convolutional neural networksBonettini Nicolò
ForensicsBestaginiAnalysis of different footprints for JPEG compression detectionChen Ke
GeophysicsBestaginiLandmine detection on GPR data employing convolutional autoencoderTesta Giuseppe
Image and videoMarcon, ParacchiniA novel tomographic approach for an early detection of multiple myeloma progressionAndrea Leggio
Image and videoMarcon, ParacchiniLimited angle computed tomography reconstruction with deep learning enhancementErbol Kasenov, Girolamo Gerace
Image and videoMarconUpper body postural assessment for common dentistry visual aidsTrotta Emilio
Image and videoTubaroReal-time tracking of electrode during deep-brain surgeryDilauro Valerio
Image and videoMarconAnalytical estimation of the error on the radius of industrial pipesLazzarin Sara
Image and videoMarcon3D reconstruction from stereo video acquired from odontoiatric microscopeSpatafora Leonardo
Image and videoMarconDenoising and classification of hyperspectral X-ray images for food quality assessmentRe Marco
Image and videoMarconA computer vision approach for assessment of dental bracket removalBehnami Arezoo
Image and videoMarconSistema per il rilevamento automatico di contaminanti alimentari basato su immagini iperspettraliRamoni Francesco
Image and videoMarconPostural assessment in dentistry by computer visionPignatelli Nicola
Multimedia forensicsBestagini, MandelliA Multi-Modal Approach to Forensic Audio-Visual Device IdentificationDavide Dal Cortivo
Music informaticsSarti, Bernardini, Borrelli, MezzaEstimating Harmonic Complexity of Chord Sequences using Transformer NetworksCecilia Morato
Music informaticsZanoni, ComanducciModeling Harmonic Complexity in Automatic Music Generation using Conditional Variational AutoencodersDavide Gioiosa
Music informaticsSarti, Borrelli, ComanducciCellular music : a novel music-generation platform based on an evolutionary paradigmMatteo Manzolini
Music informaticsSarti, BorrelliMusic emotion detection. A framework based on electrodermal activities.Gioele Pozzi
Music informaticsSarti, ComanducciTechniques for mitigating the impact of latency in
Networked Music Performance (NMP) through adaptive metronomes
Battello Riccardo
Music information retrievalSartiMusical instrument recognition: a transfer learning approachMolgora Andrea
Music information retrievalSartiUnsupervised domain adaptation for deep learning based acoustic scene classificationMezza Alessandro Ilic
Music information retrievalAntonacciAn investigation of piano transcription algorithm for jazz musicMarzorati Giorgio
Music information retrievalSartiAutomatic playlist generation using recurrent neural networkIrene Rosilde Tatiana
Music information retrievalSartiA personalized metric for music similarity using Siamese deep neural networksSala Federico
Music information retrievalSartiLearning a personalized similarity metric for musical contentCarloni Luca
Music information retrievalSartiBeat tracking using recurrent neural network : a transfer learning approachFiocchi Davide
Music information retrievalSartiPython-based framework for managing a base of complex data for music information retrievalAvocone Giuseppe
Music information retrievalSartiIndividual semantic modeling for music information retrievalAnsidei Pietro
Music information retrievalSartiChord sequences : evaluating the effect of complexity on preferenceFoscarin Francesco
Music information retrievalSartiAudio features compensation based on coding bitrateTavella Maria Stella
Musical AcousticsAntonacciModal analysis and optimization of the top plate of string instruments through a parametric control of their shapeSalvi Davide
Musical AcousticsAntonacci, Pezzoli, Malvermi An approach for Near-field Acoustic Holography based on Convolutional AutoencodersOlivieri Marco
Space-time audioAntonacci, BorraA parametric approach to virtual miking with distributed microphone arraysMarco Langè
Space-time audioAntonacci, Pezzoli, Borra, BernardiniA Deep Prior Approach to Room Impulse Response InterpolationDavide Perini
Space-time audioAntonacci, ComanducciInterpreting Deep Neural Networks Models for Acoustic Source Localization using Layer-wise Relevance PropagationAlessandro Montali
Space-time audioAntonacci, Borra, BernardiniAnalysis of Uniform Linear Arrays of Differential MicrophonesBertuletti Ivan
Space-time audioSartiA geometrical method of 3D sound spatialization for virtual reality applicationsIamele Jacopo
Space-time audioAntonacciConvolutional neural networks applied to space-time audio processing applicationsComanducci Luca
Space-time audioCancliniDenoising in the spherical harmonic domain of sound scenes acquired by compact arraysBorrelli Clara
Space-time audioAntonacciSimulazione di sistemi complessi. Case study : l'altoparlante a trombaMoscara Francesco
Space-time audioSarti, BernardiniSteerable differential microphone arraysLovatello Jacopo
Space-time audioAntonacciA plenacoustic approach to sound scene manipulationPicetti Francesco
Space-time audioAntonacciReconstruction of the soundfield in arbitrary locations using the distributed ray space transformPezzoli Mirco
Space-time audioSartiA method for HRTF personalization : weighted sparse representation synthesis of HRTFsZhu Mo
Space-time audioAntonacciRobust parametric spatial audio processing using beamforming techniquesMilano Guendalina
Space-time audioAntonacciEstimation of singing voice quality through microphone in air and contact microphoneLandini Roberta
Musical AcousticsAntonacci, MalvermiMechanical parameter estimation for vibrometric analysis and development of a low-cost platform for violin makingFederico Simeon
Space-time audioAntonacci, Comanducci3D audio with irregular microphone setups using deep learningDavide Mori
Space-time audioAntonacci, ComanducciPersonalized Sound Zone Generation using Deep LearningRoberto Alessandri