Proposals

Audio
Field
Contact
Title
Thesis type
Description
Audio signal processing
PezzoliModeling and characterization of sound source directivitiesFull thesis, Short thesisThe directivity is an inherent property of every sound source (e.g., a musical instrument). The goal of this thesis is to define suitable models for the directivity of sound sources which can be used when simulating directivities. Long or short thesis depends on the depth and novelty of the analysis. [Required knowledge: machine learning, basic knowledge of statistical signal processing, spherical harmonics decomposition of sound field].
Audio Signal Processing
Bernardini, PezzoliSpatial audio in networked music performance applicationsFull thesisIn this thesis, novel approaches to the processing of multichannel audio contents will be investigated. In particular, the main goal is to enhance the immersivity in the context of networked music performance. [Required knowledge: audio signal processing, ambisonics, binaural rendering] [Additional skills: VR]
Audio Signal Processing
Bernardini Unsupervised Speech Quality Estimation (in collaboration with Fraunhofer IIS, Erlangen)Full thesisQuantifying the quality of a speech signal is a challenging and complex problem due to the sophisticated structure of speech signals and the subjective nature of speech quality, influenced by cognitive factors. This challenge increases in the absence of a clean reference signal, a scenario of significant practical relevance. Recently, self-supervised and non-intrusive audio quality measures have gained popularity, driven by the impressive performance of self-supervised methods in tasks such as automatic speech recognition. This thesis aims to explore the application of self-supervised methods for speech signal quality estimation. Specifically, it involves obtaining subjective listening scores and validating the performance of non-intrusive speech quality measures using the acquired scores. These scores will be utilized to develop a self-supervised speech quality metric based on compressed latent space representations of the speech signal. The student undertaking this research is expected to have a foundational understanding of speech and audio signal processing, as well as deep learning. Practical skills required include proficiency in Python, along with experience in using the numpy and PyTorch libraries. [The thesis will be developed in collaboration with Fraunhofer IIS, Erlangen.]
3D audioPezzoli, GrecoNovel approach to parametric soundfield reconstructionFull thesisInvestigate a method for reconstructing sound fields at arbitrary locations using data from spatially distributed microphone arrays. Optimized for reverberant environments, this approach models the acoustic scene through parameters that define direct and diffuse sound components, capturing source location and directivity. This enables precise reconstruction, with parameters estimated in a relative coordinate system to support scalable and distributed processing.
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]
Audio signal processing
PezzoliModeling and characterization of HRTFFull thesis, Short thesisThe HRTFs are individualized acoustic characteristics of human ears. The goal of this thesis is to define suitable models for the HRTFs which can be used when simulating sound fields. Long or short thesis depends on the depth and novelty of the analysis. [Required knowledge: machine learning, basic knowledge of statistical signal processing, spherical harmonics decomposition of sound field].
3D audioPezzoli, 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
Pezzoli, OstanDevelopment 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
Pezzoli, AntonacciDeep 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, BernardiniKolmogorov-Arnold Networks for Virtual Analog ModelingFull thesisThe thesis concerns the study and implementation of Kolmogorov-Arnold neural networks for the emulation of audio circuits in the Wave Digital domain. Kolmogorov-Arnold networks reverse the Multi-layer Perceptron's paradigm by learning activation functions rather than weights and biases. We envision to apply such a fascinating theory in the context of Virtual Analog applications.
Audio signal processing
Giampiccolo, BernardiniGradient Descent Methods for the Emulation of Nonlinear Audio CircuitsFull thesisThe thesis concerns the study and implementation of gradient methods for the emulation of audio circuits in the Wave Digital domain. In fact, in presence of multiple nonlinear elements, iterative methods are needed to find the solution of nonlinear circuits. Over the past few years, several iterative techniques have been considered, namely fixed-point, Netwon-Raphson methods, etc. We propose to change paradigm and explore new ways for solving circuits in order to find the cheapest one as this is desired for the real-time emulation of audio circuits in the context of Virtual Analog applications.
Audio signal processing
Giampiccolo, BernardiniVirtual Analog Modeling, Audio Circuit Emulation, Physical Modeling Sound Synthesis through Wave Digital FiltersFull thesis
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 and radiation of the violin Long 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 behaviour of a violin for different f-hole designs/locations. Comsol experience needed.
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.
eXplainability in Generative AI and deep learning for audio and music applicationsRonchini, ComanducciEnhancing understanding and transparency in Text-to-Music generative models through eXplainable Artificial IntelligenceLong thesisResearch in text-to-music generative models is rapidly growing, reflecting the increasing interest in using AI for creative purposes. However, current models have significant flaws, and their black-box nature makes it hard to understand how they generate music or make predictions. This thesis aims to address these issues by applying eXplainable Artificial Intelligence (XAI) techniques to text-to-music and text-to-audio models. By making these models more transparent and easier to interpret, this research hopes to deepen our understanding of how they work and contribute to advancements in creative AI. If you are interested in this thesis, please send an email to luca.comanducci@polimi.it and francesca.ronchini@polimi.it

Preferred Requirements:
No requirement needed.
Experience with pytorch and deep learning packages is preferred, but not required!
Ethics in deep learning for audio and music applicationsRonchini, ComanducciConsidering ethical aspects in generative AI for audio and music applicationsLong thesisThe rapid advancement of deep learning systems has raised important ethical concerns, including issues related to increasing complexity, energy consumption, and broader societal impacts. This thesis aims to explore these ethical aspects within the context of generative AI for music or sound event detection, classification, and localization models (based on the student's interest). The research will focus on understanding the environmental and social impact of state-of-the-art models, examining critical parameters during both training and inference phases to assess their carbon footprint and/or other ethical implications (depending on the student's interests). If you are interested in this thesis, please send an email to luca.comanducci@polimi.it and francesca.ronchini@polimi.it

Preferred Requirements:
No requirement needed.
Experience with pytorch and deep learning packages is preferred, but not required!
Generative AI for audio and music applicationsRonchini, ComanducciFoley sound synthesis/Sound Scene SynthesisLong thesisThis thesis aims to explore innovative approaches for generating Foley sounds, the sound effects integrated into multimedia during post-production to enhance acoustic realism. With growing interest in AI-driven sound synthesis, generative models offer new possibilities for automating and enriching Foley sound production. The scope of this research includes investigating various techniques to generate original audio clips across diverse sound categories, with the goal of encanching deep learning techniques for Foley Sound Generation. If you are interested in this thesis, please send an email to luca.comanducci@polimi.it and francesca.ronchini@polimi.it

Preferred Requirements:
No requirement needed.
Experience with pytorch and deep learning packages is preferred, but not required!
Human-AI interaction in music domainRonchini, ComanducciInvestigating Human-AI interaction in the text-guided generative models domain: how text-to-music model can be integrate in the creative process of music pratictioners?Long thesisThis thesis aims to investigate human-AI interaction in the context of text-guided generative models, focusing on how these systems can realistically be integrated into creative practices. While text-to-music models represent a significant breakthrough, their practical use by musicians and practitioners remains an open question. This research will explore how users interact with these models, studying their impact on creativity and satisfaction. The project will include user experience studies and the development of tools to enable personalized music generation, evaluating how effectively these systems meet artistic needs. If you are interested in this thesis, please send an email to. If you are interested in this thesis, please send an email to luca.comanducci@polimi.it and francesca.ronchini@polimi.it

Preferred Requirements:
No requirement needed.
Experience with pytorch and deep learning packages is preferred, but not required!
Generative AI for audio and music applicationRonchini, ComanducciExploring Text-to-Audio/Text-to-Music Models for Audio and Music ApplicationsLong thesisThis thesis aims to explore the efficient integration of text-to-audio and text-to-music models for various audio and music applications. While text-to-audio models have the ability to transform textual inputs into high-quality audio outputs, certain areas of application remain underexplored, and new ways to leverage these models across different contexts need further investigation. The research will focus on how these generative models can be safely integrated into society, how end-users can benefit from them, and how to enhance and apply them in previously unexplored domains. If you are interested in this thesis, please send an email to luca.comanducci@polimi.it and francesca.ronchini@polimi.it

Preferred Requirements:
No requirement needed.
Experience with pytorch and deep learning packages is preferred, but not required!
Music Informatics/Human-computer interactionRonchini, ComanducciINTERACTION DESIGN FOR LIVE PERFORMANCE OF ACOUSMATIC MUSIC (In collaboration with Fondazione Culturale San Fedele)Long thesisExperimental music and technology developments have always went hand in hand, from the development of the synthesizer to the recent introduction of artificial intelligence in music production practices. INNER SPACES is a series of events related to experimental electronic music and audiovisual arts realized in the exclusive context of the San Fedele Auditorium, in Milan. The objective of the thesis is to develop tools for augmented performance in the context of acousmatic/electronic music, with the possibility of including the developed software in the second season of INNER SPACES (spring). While the project will be focused on scientific developments related to audio and programming, a keen interest and motivation for art and experimental music is highly desired. For any information please contact: luca.comanducci@polimi.it and francesca.ronchini@polimi.it


Preferred Requirements:
Experience in python/supercollider/MaxMsp
Interest for acousmatic/experimental electronic music
Image and Video
Field
Contact
Title
Thesis type
Description
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, GigantiEnhancement of emission mapsFullAccurate BVOC emission maps are crucial for understanding their effects on air quality and climate, yet existing maps often lack the spatial resolution needed for detailed analysis. This thesis proposes using Super-Resolution Neural Networks (SRNNs) to enhance these maps by generating high-resolution data from low-resolution inputs. SRNNs can capture finer spatial details and improve the accuracy of emission maps, bridging gaps in sparse data to support high-precision environmental modeling.
Spatiotemporal processingBestagini, Mandelli, GigantiSpatiotemporal analysis of climate dataFullClimate data analysis is hindered by complex patterns and frequent data gaps. This thesis proposes using Spatiotemporal Graph Neural Networks (STGNNs) to improve climate forecasting and data imputation by capturing spatial and temporal relationships. By testing STGNNs for predicting future climate variables and filling missing data, this research aims to enhance data accuracy and reliability in climate modeling.
Geophysics
Contact
Title
Thesis type
Description
Tubaro, BestaginiImproving 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, BestaginiDenoising 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, BestaginiMachine 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?

Currently on-going

Expand list
Field
Supervisor
Topic
Student(s)
Musical acousticsPezzoli, MalvermiStatistical 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
3D audioPezzoli, OstanAcoustic 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
Audio signal processing
Massi, Giampiccolo, BernardiniOptimization of MEMs Loudspeaker circuital models via Automatic DifferentiationLelio Casale
Space-time audioPezzoli, GrecoSound field reconstruction for 6DoF navigationSilvio Attolini
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
Music informaticsSarti, Mezza, BernardiniUnsupervised selection of harmonic complexity metricsGiorgio De Luca
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
Music InformaticsSarti, ComanducciHandMonizer, personalized digital musical instrument designAntonios Pappas
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
Generative AI for audio/musicRonchini, ComanducciGenerative Controllable Neural Audio Synthesis


Simone Marcucci
DCASERonchini, Comanducci, CobosSound Event Detection and Localization using Mel-FSGCCFederico Angelo Luigi Ferreri
Generative AI for audio/musicRonchini, ComanducciTimbre TransferGuglielmo Fraticcioli
DCASEComanducciBioacoustic detection Nicolò Pisanu

Past (from 2017)

Expand list
Field
Supervisor
Title
Student(s)
Link
Space-time audioPezzoli, ComanducciGenerative Models for HRTF predictionJuan Camilo Albarracín Sánchez
Space-time audioPezzoli, MiotelloSpherical microphone array upsamplingFerdinando Terminiello
3D audioPezzoli, MalvermiNeural Network-based representation of sound source directivityEdoardo Morena
Musical acousticsPezzoliNearfield Acoustic Holography solver based on Physics-Informed Neural NetworkXinmeng Luan
Space-time audioPezzoli, MiotelloReal-time microphone array rendering framework for binaural reproductionPaolo Ostan
Music InformaticsComanducci, MezzaImpact of velocity on drum patterns perceived complexityGabriele Maucione
Audio signal processingGiampiccolo, BernardiniWave Digital Models of Nonlinear Piezoelectric LoudspeakersArmando Boemiohttps://www.politesi.polimi.it/handle/10589/218000
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 Parrahttps://www.politesi.polimi.it/handle/10589/214898
Music InformaticsZanoni, ComanducciProcedural Music Generation For Video gamesFrancesco Zumerlehttps://www.politesi.polimi.it/handle/10589/210809
Audio signal processingBernardini, Giampiccolo, MezzaOn the Use of Fundamental Frequency Estimation for Virtual Bass EnhancementFabio Spreaficohttps://www.politesi.polimi.it/handle/10589/210018
Image forensicsBestagini, MandelliManipulation detection for scientific imagesGiovanni Zanocco
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 Boarinohttps://www.politesi.polimi.it/handle/10589/211644
Audio signal processingBernardini, GiampiccoloA Wave Digital Extended Fixed-Point Method for Virtual Analog ApplicationsDavide Marin Pasinhttps://www.politesi.polimi.it/handle/10589/212614
Space-time audioAntonacci, Pezzoli DIRECTION OF ARRIVAL ESTIMATION USING CONVOLUTIONAL RECURRENT NEURAL NETWORK WITH RELATIVE HARMONIC COEFFICIENTS AND TRIPLET LOSS IN NOISY AND REVERBERATING ENVIRONMENTSLuca Cattaneohttps://www.politesi.polimi.it/handle/10589/208311
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 Gobbatohttps://www.politesi.polimi.it/handle/10589/198537
Musical AcousticsSarti, Paoletti, Adali, MalvermiAcoustic Characterization of materialsMarco Donzelli
Music Informatics Zanoni, ComanducciDeep Learning-based Timbre TransferSilvio Polhttps://www.politesi.polimi.it/handle/10589/189682
Audio signal processingAntonacci, Pezzoli, BorraA perceptual evaluation of sound field reconstruction algorithmsMiriam Papagnohttps://www.politesi.polimi.it/handle/10589/186341
Audio signal processing
Bernardini, GiampiccoloCharacterization of Small-Size Loudspeakers for Mobile ApplicationsSamuele Buonassisihttps://www.politesi.polimi.it/handle/10589/189746
Image forensicsBestagini, CannasEnhanced Amplitude SAR Imagery Splicing Localization through Land Cover Mapping TechniquesEmanuele Intagliata
GeophysicsBestagini, LipariSalt Segmentation of Geophysical Images through Explainable CNNsFrancesco Maffezzoli
Music informaticsSarti, BorrelliConnecting NN to bio-metric signalsJoep Rene Wulms
Audio forensicsBestagini, BorrelliA metric learning approach for splicing localization based on synthetic speech detectionFrancesco Castellihttps://www.politesi.polimi.it/handle/10589/184332
Audio forensicsBestagini, BorrelliCombining automatic speaker verification and prosody analysis for synthetic speech detectionLuigi Attorresihttps://www.politesi.polimi.it/handle/10589/187094
Music informaticsZanoni, BorrelliSocial interaction based music recommendation systemCarlo Pulvirenti
Music informaticsBestagini, CuccovilloSpeech fingerprinting and matching for content retrievalLaura Colzanihttps://www.politesi.polimi.it/handle/10589/187212
Musical AcousticsAntonacci, OlivieriTowards white-box data-driven methods for Near-field Acoustic HolographyHagar Kafri
Video forensicsBestaginiA CNN-based detector for video frame-rate interpolationSimone Marianihttps://www.politesi.polimi.it/handle/10589/186433
Image/video processingBestaginiAudio-video techniques for the analysis of players behaviour in Badminton matchesSamuele Bosihttps://www.politesi.polimi.it/handle/10589/186571
Video forensicsBestagini, MandelliForensic detection of deepfakes generated through video-to-video translationCarmelo Fascellahttps://www.politesi.polimi.it/handle/10589/182988
Audio signal processing
Bernardini, Mezza, GiampiccoloWave Digital Filter Modeling of Audio Circuits with Hysteresis Nonlinearities using Neural NetworksOliviero Massihttps://www.politesi.polimi.it/handle/10589/186739
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 Scerbohttps://www.politesi.polimi.it/handle/10589/176160
Music informaticsSarti, BorrelliHarmonic complexity estimation of jazz musicGiovanni Agosti
Audio forensicsAntonacci, BorrelliA model selection method for room shape classification based on mono speech signalsGabriele Antonaccihttps://www.politesi.polimi.it/handle/10589/179887
Audio forensicsBestaginiAudio splicing detection and localization based on recording device cuesDaniele Ugo Leonziohttps://www.politesi.polimi.it/handle/10589/179424
Audio forensicsBestaginiSpeaker-Independent Microphone Identification via Blind Channel Estimation in Noisy ConditionAntonio Gigantihttps://www.politesi.polimi.it/handle/10589/179420
Audio forensicsBestagini, BorrelliSynthetic Speech Detection through Convolutional Neural Networks in Noisy EnvironmentsEleonora Landinihttps://www.politesi.polimi.it/handle/10589/179458
Audio forensicsBestagini, Borrelli, SalviSynthetic speech detection based on sentiment analysisEmanuele Contihttps://www.politesi.polimi.it/handle/10589/177968
Multimedia forensicsBestagini, Salvi, BorrelliAudio-video deepfake detection through emotion recognitionJacopo Ginohttps://www.politesi.polimi.it/handle/10589/179037
Audio signal processingSarti, Giampiccolo, BernardiniParallel Wave Digital Implementations of Nonlinear Audio CircuitsNatoli Antoninohttps://www.politesi.polimi.it/handle/10589/178037
Musical AcousticsAntonacci, MalvermiData driven methods for frequency response functions interpolationMatteo Acerbihttps://www.politesi.polimi.it/handle/10589/170179
Audio forensicsBestagini, MandelliTime-Scaling Detection in Audio RecordingsMichele Piliahttps://www.politesi.polimi.it/handle/10589/173711
Audio forensicsBestagini, BorrelliSpeech Intelligibility Parameters Estimation Through Convolutional Neural NetworksMattia Papahttps://www.politesi.polimi.it/handle/10589/173756
Audio forensicsAntonacciClosed and open set classification of real and AI synthesised speechMichelangelo Medorihttps://www.politesi.polimi.it/handle/10589/170094
Audio forensicsAntonacciAn approach to room volume estimation from single-channel speech signals based on neural networksCastelnuovo Carlohttps://www.politesi.polimi.it/handle/10589/164749
Audio forensicsBestaginiAudio Splicing Detection and Localization Based on Acoustic CuesCapoferri Davidehttps://www.politesi.polimi.it/handle/10589/164950
Audio processingSarti, ComanducciAudio frame reconstruction from incomplete observations using Deep Learning techniquesSchils Minh Cédrichttps://matheo.uliege.be/handle/2268.2/10138
Audio processingSarti, BernardiniWave Digital Modeling and Simulation of Nonlinear Electromagnetic CircuitsGiampiccolo Riccardohttps://www.politesi.polimi.it/handle/10589/153994
Audio processingSarti, BernardiniAntiderivative Antialiasing in Nonlinear Wave Digital FiltersAlbertini Davidehttps://www.politesi.polimi.it/handle/10589/152934
Audio processingSarti, BernardiniWave Digital Implementation of Nonlinear Audio Circuits based on the Scattering Iterative MethodProverbio Alessandrohttps://www.politesi.polimi.it/handle/10589/152323
Audio processingAntonacciA system for super resolution vibrometric analysis through convolutional neural networksCampagnoli Chiarahttps://www.politesi.polimi.it/handle/10589/152613
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 Lucahttps://www.politesi.polimi.it/handle/10589/150531
Audio processingBestaginiDNN based post-filtering for quality improvement of AMR-WB decoded speechGupta Kishanhttps://www.politesi.polimi.it/handle/10589/151000
Audio processingSartiStudio sull'implementazione degli algoritmi per il musical instruments ed il sound reinforcement basato su un processore multicoreAretino Michelehttps://www.politesi.polimi.it/handle/10589/139079
Audio processingSarti, BernardiniModeling nonlinear 3-terminal devices in the wave digital domainVergani Alessio Emanuelehttps://www.politesi.polimi.it/handle/10589/133184
ForensicsBestaginiConvolutional and recurrent neural networks for video tampering detection and localizationCannas Edoardo Danielehttps://www.politesi.polimi.it/handle/10589/149900
ForensicsBestaginiA study on Bagging-Voronoi algorithm for tampering localizationCereghetti Corinne Elenahttps://www.politesi.polimi.it/handle/10589/141725
ForensicsBestaginiJPEG-based forensics through convolutional neural networksBonettini Nicolòhttps://www.politesi.polimi.it/handle/10589/133727
ForensicsBestaginiAnalysis of different footprints for JPEG compression detectionChen Kehttps://www.politesi.polimi.it/handle/10589/132721
GeophysicsBestaginiLandmine detection on GPR data employing convolutional autoencoderTesta Giuseppehttps://www.politesi.polimi.it/handle/10589/142106
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 Emiliohttps://www.politesi.polimi.it/handle/10589/145563
Image and videoTubaroReal-time tracking of electrode during deep-brain surgeryDilauro Valeriohttps://www.politesi.polimi.it/handle/10589/144685
Image and videoMarconAnalytical estimation of the error on the radius of industrial pipesLazzarin Sarahttps://www.politesi.polimi.it/handle/10589/144394
Image and videoMarcon3D reconstruction from stereo video acquired from odontoiatric microscopeSpatafora Leonardohttps://www.politesi.polimi.it/handle/10589/143780
Image and videoMarconDenoising and classification of hyperspectral X-ray images for food quality assessmentRe Marcohttps://www.politesi.polimi.it/handle/10589/142922
Image and videoMarconA computer vision approach for assessment of dental bracket removalBehnami Arezoohttps://www.politesi.polimi.it/handle/10589/142362
Image and videoMarconSistema per il rilevamento automatico di contaminanti alimentari basato su immagini iperspettraliRamoni Francescohttps://www.politesi.polimi.it/handle/10589/135891
Image and videoMarconPostural assessment in dentistry by computer visionPignatelli Nicolahttps://www.politesi.polimi.it/handle/10589/135030
Multimedia forensicsBestagini, MandelliA Multi-Modal Approach to Forensic Audio-Visual Device IdentificationDavide Dal Cortivohttps://www.politesi.polimi.it/handle/10589/175593
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 Manzolinihttps://www.politesi.polimi.it/handle/10589/167291
Music informaticsSarti, BorrelliMusic emotion detection. A framework based on electrodermal activities.Gioele Pozzihttps://www.politesi.polimi.it/handle/10589/152931
Music informaticsSarti, ComanducciTechniques for mitigating the impact of latency in
Networked Music Performance (NMP) through adaptive metronomes
Battello Riccardohttps://www.politesi.polimi.it/handle/10589/152923
Music information retrievalSartiMusical instrument recognition: a transfer learning approachMolgora Andreahttps://www.politesi.polimi.it/handle/10589/147383
Music information retrievalSartiUnsupervised domain adaptation for deep learning based acoustic scene classificationMezza Alessandro Ilichttps://www.politesi.polimi.it/handle/10589/145573
Music information retrievalAntonacciAn investigation of piano transcription algorithm for jazz musicMarzorati Giorgiohttps://www.politesi.polimi.it/handle/10589/144745
Music information retrievalSartiAutomatic playlist generation using recurrent neural networkIrene Rosilde Tatianahttps://www.politesi.polimi.it/handle/10589/142101
Music information retrievalSartiA personalized metric for music similarity using Siamese deep neural networksSala Federicohttps://www.politesi.polimi.it/handle/10589/139078
Music information retrievalSartiLearning a personalized similarity metric for musical contentCarloni Lucahttps://www.politesi.polimi.it/handle/10589/139076
Music information retrievalSartiBeat tracking using recurrent neural network : a transfer learning approachFiocchi Davidehttps://www.politesi.polimi.it/handle/10589/139073
Music information retrievalSartiPython-based framework for managing a base of complex data for music information retrievalAvocone Giuseppehttps://www.politesi.polimi.it/handle/10589/138449
Music information retrievalSartiIndividual semantic modeling for music information retrievalAnsidei Pietrohttps://www.politesi.polimi.it/handle/10589/137160
Music information retrievalSartiChord sequences : evaluating the effect of complexity on preferenceFoscarin Francescohttps://www.politesi.polimi.it/handle/10589/136448
Music information retrievalSartiAudio features compensation based on coding bitrateTavella Maria Stellahttps://www.politesi.polimi.it/handle/10589/134607
Musical AcousticsAntonacciModal analysis and optimization of the top plate of string instruments through a parametric control of their shapeSalvi Davidehttps://www.politesi.polimi.it/handle/10589/166557
Musical AcousticsAntonacci, Pezzoli, Malvermi An approach for Near-field Acoustic Holography based on Convolutional AutoencodersOlivieri Marcohttps://www.politesi.polimi.it/handle/10589/167039
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 Perinihttps://www.politesi.polimi.it/handle/10589/175583
Space-time audioAntonacci, ComanducciInterpreting Deep Neural Networks Models for Acoustic Source Localization using Layer-wise Relevance PropagationAlessandro Montalihttps://www.politesi.polimi.it/handle/10589/169239
Space-time audioAntonacci, Borra, BernardiniAnalysis of Uniform Linear Arrays of Differential MicrophonesBertuletti Ivanhttps://www.politesi.polimi.it/handle/10589/154604
Space-time audioSartiA geometrical method of 3D sound spatialization for virtual reality applicationsIamele Jacopohttps://www.politesi.polimi.it/handle/10589/143770
Space-time audioAntonacciConvolutional neural networks applied to space-time audio processing applicationsComanducci Lucahttps://www.politesi.polimi.it/handle/10589/139077
Space-time audioCancliniDenoising in the spherical harmonic domain of sound scenes acquired by compact arraysBorrelli Clarahttps://www.politesi.polimi.it/handle/10589/139075
Space-time audioAntonacciSimulazione di sistemi complessi. Case study : l'altoparlante a trombaMoscara Francescohttps://www.politesi.polimi.it/handle/10589/139074
Space-time audioSarti, BernardiniSteerable differential microphone arraysLovatello Jacopohttps://www.politesi.polimi.it/handle/10589/139072
Space-time audioAntonacciA plenacoustic approach to sound scene manipulationPicetti Francescohttps://www.politesi.polimi.it/handle/10589/138430
Space-time audioAntonacciReconstruction of the soundfield in arbitrary locations using the distributed ray space transformPezzoli Mircohttps://www.politesi.polimi.it/handle/10589/136447
Space-time audioSartiA method for HRTF personalization : weighted sparse representation synthesis of HRTFsZhu Mohttps://www.politesi.polimi.it/handle/10589/135952
Space-time audioAntonacciRobust parametric spatial audio processing using beamforming techniquesMilano Guendalinahttps://www.politesi.polimi.it/handle/10589/134609
Space-time audioAntonacciEstimation of singing voice quality through microphone in air and contact microphoneLandini Robertahttps://www.politesi.polimi.it/handle/10589/134604
Musical AcousticsAntonacci, MalvermiMechanical parameter estimation for vibrometric analysis and development of a low-cost platform for violin makingFederico Simeonhttps://www.politesi.polimi.it/handle/10589/170995
Space-time audioAntonacci, Comanducci3D audio with irregular microphone setups using deep learningDavide Morihttps://www.politesi.polimi.it/handle/10589/175608
Space-time audioAntonacci, ComanducciPersonalized Sound Zone Generation using Deep LearningRoberto Alessandrihttps://www.politesi.polimi.it/handle/10589/203852