Proposals
Audio
Field | Contact | Title | Thesis type | Description |
|---|---|---|---|---|
| 3D audio | Pezzoli | Physics-informed deep prior for sound field reconstruction | Full thesis | The 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 audio | Pezzoli | Ray-space-based kernel interpolation | Full thesis | Sound 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 audio | Pezzoli | Physics-informed deep kernel interpolation for sound field reconstruction | Full thesis | Sound 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 | Bernardini | Strategies for Clipping Prevention in Dynamic Sound Filtering | Full 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, Bernardini | Deep Packet Loss Concealment for Speech and Music | Full thesis | Audio 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, Bernardini | Music Demixing | Full thesis | Music 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 audio | Olivieri, Pezzoli, Antonacci | Nearfield filter for spherical microphone array recordings | Full thesis | Spherical 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, Antonacci | Development of acoustic simulation framework for GPU | Short thesis | Parallel 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 Acoustics | Pezzoli, Cillo, Longo | Enhancement of a reduced-order finite-element model of a classical guitar | Full/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 forensics | Bestagini, Antonacci | Detection of text-to-speech algorithms | Full thesis | Nowadays, 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, Pezzoli | Deep learning solution for localization of acoustic sources in the spherical harmonics domain | Full thesis | The 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, Bernardini | Virtual Analog Modeling, Audio Circuit Emulation, Physical Modeling Sound Synthesis through Wave Digital Filters | Full thesis | |
| Differential Microphone Arrays | Bernardini, Albertini | Two-Stage Differential Beamforming over Networks of Microphone Arrays | Full 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 Acoustics | Antonacci, Olivieri, Pezzoli | Transfer Learning techniques for Nearfield Acoustic Holography analysis | Full/short thesis | Recent 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 Acoustics | Gonzalez, Malvermi, Antonacci | Experimental measurement and construction of violin top plates | Full/short thesis | The 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 Acoustics | Gonzalez, Antonacci | Role of f-hole design in stress distribution on the violin top plate | Long thesis | The 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 Acoustics | Gonzalez, Malvermi, Antonacci | Effect of tailpiece height in the acoustic response of a violin | Long thesis | Varying 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 Acoustics | Gonzalez, Longo, Antonacci | Linear interpolation between shapes in western guitars | Long thesis | In 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 Acoustics | Gonzalez, Greco, Antonacci | Timbral Study of 3D printed organ pipes | Long thesis | Recently, 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 Acoustics | Gonzalez, Malvermi, Antonacci | Experimental study of wooden metamaterials | Long thesis | Experimental 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 Acoustics | Gonzalez, Antonacci | Developing a new Manouche guitar: studying different bracings models for the gypsy jazz icon | Long thesis | Manouche 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 Acoustics | Greco, Antonacci | Neural Network-Based Prediction of Woodwind Mouthpiece Sound Characteristics through Finite Element Method Simulations | Long thesis | This 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 music | Ronchini, Comanducci | Enhancing understanding and transparency in Text-to-Music generative models through eXplainable Artificial Intelligence | Long thesis | The 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 audio | Ronchini, Comanducci | Balance between performance end carbon footspring of state-of-the-art deep learning systems for audio domain applications | Long thesis | The 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 Audio | Ronchini, Comanducci | Language-Queried Audio Source Separation | Long thesis | The 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 audio | Ronchini, Comanducci | Foley sound synthesis/Sound Scene Synthesis | Long thesis | Foley 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 Localization | Ronchini, Comanducci | Audio and Audiovisual Sound Event Localization and Detection with Source Distance Estimation | Long thesis | The 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 audio | Ronchini, Comanducci | Exploring Text-to-Audio Models for Audio Data Training in System Development | Long thesis | This 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
Field | Contact | Title | Thesis type | Description |
|---|---|---|---|---|
| Image/video forensics | Bestagini, Mandelli, Cannas | Detect and localize image and video manipulations | Full | Images 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 forensics | Bestagini, Mandelli, Cannas | Distinguish original videos from DeepFakes | Full | DeepFake 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 forensics | Bestagini, Mandelli, Cannas | Assess the authenticity of satellite images | Full | Satellites 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 forensics | Bestagini, Mandelli, Cannas | Forensic analysis of scientific images | Full | Scientific 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 processing | Bestagini, Mandelli, Giganti | Enhanceent of emission maps | Full | Biogenic 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. |
Geophysics
Contact | Title | Thesis type | Description |
|---|---|---|---|
| Tubaro, Lipari | Improving Full Waveform Inversion with CNNs | Full/short | Full 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, Lipari | Denoising and Interpolation of seismic data through CNNs | Full/short | The 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, Lipari | Machine Learning guided Seismic Interpretation | Full/short | Human 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, Lipari | Physics-aware Transfer Learning | Full/short | Can a neural network learn Physics? |
| Tubaro, Lipari | Regularizing Traveltime Tomography via Machine Learning | Full/short | Traveltime 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
Expand list
Field | Supervisor | Topic | Student(s) |
|---|---|---|---|
| Musical acoustics | Pezzoli, Malvermi | Statiscal charcterization of directivity | Gian Marco Ricci |
| Musical acoustics | Pezzoli, Malvermi | Deep prior based vibroacustic analysis | Riccardo Sebastiani Croce |
| Musical acoustics | Pezzoli, Malvermi | PINN based vibroacoustic analysis | Federico Zese |
| 3D audio | Pezzoli, Greco | Localization of sound sources using spherical harmonics | Silvia Messena |
| Musical acoustics | Olivieri, Pezzoli | Nearfield Acoustic Holography solver based on Physics-Informed Neural Network | Xinmeng Luan |
| Audio signal processing | Giampiccolo, Bernardini | 2D Canonical Piecewise-Linear functions for the Wave Digital Modeling of 2-port Nonlinearities | Valerio Maiolo |
| 3D audio | Pezzoli | Acoustic Virtual Reality evaluation system | Francesca Del Gaudio |
| Audio signal processing | Massi, Giampiccolo, Bernardini | Deep Learning Models of Nonlinear Time-Varying Circuits in the Wave Digital Domain | Shijie Yang |
| Audio signal processing | Giampiccolo, Bernardini | Automatic Generation of VSTs based on WDFs | Stefano Ravasi |
| Audio signal processing | Giampiccolo, Bernardini | Modeling Circuits with Two Multiport Nonlinearities | Sebastian Gafencu |
| Audio signal processing | Giampiccolo, Bernardini | Modeling of MOSFETs for Virtual Analog Applications | Marco Ferrè |
| 3D audio | Pezzoli, Malvermi | Neural Network-based representation of sound source directivity | Edoardo Morena |
| Audio signal processing | Massi, Giampiccolo, Bernardini | Optimization of MEMs Loudspeaker circuital models via Automatic Differentiation | Lelio Casale |
| Space-time audio | Pezzoli, Miotello | Spherical microphone array upsampling | Ferdinando Terminiello |
| Space-time audio | Pezzoli, Greco | Sound field reconstruction for 6DoF navigation | Silvio Attolini |
| Space-time audio | Pezzoli, Miotello | Real-time microphone array rendering framework for binaural reproduction | Paolo Ostan |
| Space-time audio | Antonacci, Pezzoli | Sound field separation in the spherical harmonics domain | Sagi Della-Torre |
| Audio signal processing | Giampiccolo, Massi, Bernardini | Vacuum Tubes Modeling by means of Neural Networks in the Wave Digital Domain | Genis Casanova |
| Audio signal processing | Giampiccolo, Bernardini | Modeling of Nonlinear Piezoelectric Loudspeakers | Armando Boemio |
| Image forensics | Bestagini, Mandelli | Manipulation detection for scientific images | Giovanni Zanocco |
| Music informatics | Sarti, Mezza, Bernardini | Unsupervised selection of harmonic complexity metrics | Giorgio De Luca |
| Music informatics | Zanoni, Borrelli | Social interaction based music recommendation system | Carlo Pulvirenti |
| Music informatics | Sarti, Borrelli | Connecting NN to bio-metric signals | Joep Rene Wulms |
| Musical Acoustics | Gonzalez, Antonacci | Random variation of guitar bracings | Mattia Vanessa |
| Musical acoustics | Gonzalez, Antonacci | Metamaterials for guitarmaking | Gabriele Marelli, Mattia Lercari |
| Musical acoustics / AI | Gonzalez, Antonacci | AI-powered pick up: making guitars sound great again | Emanuele Voltini |
| Space-time audio | Pezzoli, Comanducci | Generative Models for HRTF prediction | Juan Camilo Albarracín Sánchez |
| Music Informatics | Sarti, Comanducci | HandMonizer, personalized digital musical instrument design | Antonios Pappas |
| Music Informatics | Comanducci, Mezza | Impact of velocity on drum patterns perceived complexity | Gabriele Maucione |
| Music Informatics | Comanducci, Ronchini, Zanoni | Personalized Music Generation using text-to-music models | Gabriele Perego |
| Generative AI for audio | Comanducci, Ronchini | Adding temporal information and event order modeling to generative models for audio/music | Marco Furio Colombo |
Past (from 2017)
Expand list
Field | Supervisor | Title | Student(s) | Link |
|---|---|---|---|---|
| Space-time audio | Pezzoli | Analysis of the directivity of sound sources | Hou Hin Au-Yeung | |
| Audio signal processing | Bernardini, Giampiccolo, Albertini | Application of antiderivative antialiasing to MOSFET elements in wave digital filters | Christian Parra | https://www.politesi.polimi.it/handle/10589/214898 |
| Music Informatics | Zanoni, Comanducci | Procedural Music Generation For Video games | Francesco Zumerle | https://www.politesi.polimi.it/handle/10589/210809 |
| Audio signal processing | Bernardini, Giampiccolo, Mezza | On the Use of Fundamental Frequency Estimation for Virtual Bass Enhancement | Fabio Spreafico | https://www.politesi.polimi.it/handle/10589/210018 |
| Video forensics | Bestagini, Cannas | Deepfake video detection through multi-look analysis | Adriano Bonfantini | |
| Video processing | Bestagini, Redondi | Automatic video analysis of badminton matches | Ivan Motasov | |
| Space-time audio | Bernardini, Giampiccolo, Mezza | Designing of Scattering Delay Networks Via Automatic Differentiation | Francesco Boarino | https://www.politesi.polimi.it/handle/10589/211644 |
| Audio signal processing | Bernardini, Giampiccolo | A Wave Digital Extended Fixed-Point Method for Virtual Analog Applications | Davide Marin Pasin | https://www.politesi.polimi.it/handle/10589/212614 |
| Space-time audio | Antonacci, Pezzoli | DIRECTION OF ARRIVAL ESTIMATION USING CONVOLUTIONAL RECURRENT NEURAL NETWORK WITH RELATIVE HARMONIC COEFFICIENTS AND TRIPLET LOSS IN NOISY AND REVERBERATING ENVIRONMENTS | Luca Cattaneo | https://www.politesi.polimi.it/handle/10589/208311 |
| Musical Acoustics | Ripamonti, Malvermi, Gonzalez | Experimental Validation for data-driven Near-field Acoustic Holography | Alessio Lampis | |
| Musical Acoustics | Antonacci, Malvermi | Improved sensors for low-cost Vibrometric Kit | Fabio Guarnieri | |
| Audio signal processing | Bernardini, Giampiccolo | A Wave Digital Hierarchical Quasi-Newton Method for Virtual Analog Modeling | Luca Gobbato | https://www.politesi.polimi.it/handle/10589/198537 |
| Musical Acoustics | Sarti, Paoletti, Adali, Malvermi | Acoustic Characterization of materials | Marco Donzelli | |
| Music Informatics | Zanoni, Comanducci | Deep Learning-based Timbre Transfer | Silvio Pol | https://www.politesi.polimi.it/handle/10589/189682 |
| Audio signal processing | Antonacci, Pezzoli, Borra | A perceptual evaluation of sound field reconstruction algorithms | Miriam Papagno | https://www.politesi.polimi.it/handle/10589/186341 |
| Audio signal processing | Bernardini, Giampiccolo | Characterization of Small-Size Loudspeakers for Mobile Applications | Samuele Buonassisi | https://www.politesi.polimi.it/handle/10589/189746 |
| Image forensics | Bestagini, Cannas | Enhanced Amplitude SAR Imagery Splicing Localization through Land Cover Mapping Techniques | Emanuele Intagliata | |
| Geophysics | Bestagini, Lipari | Salt Segmentation of Geophysical Images through Explainable CNNs | Francesco Maffezzoli | |
| Audio forensics | Bestagini, Borrelli | A metric learning approach for splicing localization based on synthetic speech detection | Francesco Castelli | https://www.politesi.polimi.it/handle/10589/184332 |
| Audio forensics | Bestagini, Borrelli | Combining automatic speaker verification and prosody analysis for synthetic speech detection | Luigi Attorresi | https://www.politesi.polimi.it/handle/10589/187094 |
| Music informatics | Bestagini, Cuccovillo | Speech fingerprinting and matching for content retrieval | Laura Colzani | https://www.politesi.polimi.it/handle/10589/187212 |
| Musical Acoustics | Antonacci, Olivieri | Towards white-box data-driven methods for Near-field Acoustic Holography | Hagar Kafri | |
| Video forensics | Bestagini | A CNN-based detector for video frame-rate interpolation | Simone Mariani | https://www.politesi.polimi.it/handle/10589/186433 |
| Image/video processing | Bestagini | Audio-video techniques for the analysis of players behaviour in Badminton matches | Samuele Bosi | https://www.politesi.polimi.it/handle/10589/186571 |
| Video forensics | Bestagini, Mandelli | Forensic detection of deepfakes generated through video-to-video translation | Carmelo Fascella | https://www.politesi.polimi.it/handle/10589/182988 |
| Audio signal processing | Bernardini, Mezza, Giampiccolo | Wave Digital Filter Modeling of Audio Circuits with Hysteresis Nonlinearities using Neural Networks | Oliviero Massi | https://www.politesi.polimi.it/handle/10589/186739 |
| Music informatics | Antonacci, Pezzoli, Comanducci | Deep Prior Audio Inpainting | Federico Miotello | |
| Audio signal processing | Bestagini, Buccoli | Low-latency speaker recognition | Francesco Salani | |
| Video forensics | Bestagini, Bonettini | A Data Driven Approach to Deepfake Detection via Feature Analysis Based on Limited Data | Bingyang Hu | |
| Space-time audio | Antonacci, Borrelli, Borra | Beamforming and Speaker Identification through Deep Neural Networks | Matteo Scerbo | https://www.politesi.polimi.it/handle/10589/176160 |
| Music informatics | Sarti, Borrelli | Harmonic complexity estimation of jazz music | Giovanni Agosti | |
| Audio forensics | Antonacci, Borrelli | A model selection method for room shape classification based on mono speech signals | Gabriele Antonacci | https://www.politesi.polimi.it/handle/10589/179887 |
| Audio forensics | Bestagini | Audio splicing detection and localization based on recording device cues | Daniele Ugo Leonzio | https://www.politesi.polimi.it/handle/10589/179424 |
| Audio forensics | Bestagini | Speaker-Independent Microphone Identification via Blind Channel Estimation in Noisy Condition | Antonio Giganti | https://www.politesi.polimi.it/handle/10589/179420 |
| Audio forensics | Bestagini, Borrelli | Synthetic Speech Detection through Convolutional Neural Networks in Noisy Environments | Eleonora Landini | https://www.politesi.polimi.it/handle/10589/179458 |
| Audio forensics | Bestagini, Borrelli, Salvi | Synthetic speech detection based on sentiment analysis | Emanuele Conti | https://www.politesi.polimi.it/handle/10589/177968 |
| Multimedia forensics | Bestagini, Salvi, Borrelli | Audio-video deepfake detection through emotion recognition | Jacopo Gino | https://www.politesi.polimi.it/handle/10589/179037 |
| Audio signal processing | Sarti, Giampiccolo, Bernardini | Parallel Wave Digital Implementations of Nonlinear Audio Circuits | Natoli Antonino | https://www.politesi.polimi.it/handle/10589/178037 |
| Musical Acoustics | Antonacci, Malvermi | Data driven methods for frequency response functions interpolation | Matteo Acerbi | https://www.politesi.polimi.it/handle/10589/170179 |
| Audio forensics | Bestagini, Mandelli | Time-Scaling Detection in Audio Recordings | Michele Pilia | https://www.politesi.polimi.it/handle/10589/173711 |
| Audio forensics | Bestagini, Borrelli | Speech Intelligibility Parameters Estimation Through Convolutional Neural Networks | Mattia Papa | https://www.politesi.polimi.it/handle/10589/173756 |
| Audio forensics | Antonacci | Closed and open set classification of real and AI synthesised speech | Michelangelo Medori | https://www.politesi.polimi.it/handle/10589/170094 |
| Audio forensics | Antonacci | An approach to room volume estimation from single-channel speech signals based on neural networks | Castelnuovo Carlo | https://www.politesi.polimi.it/handle/10589/164749 |
| Audio forensics | Bestagini | Audio Splicing Detection and Localization Based on Acoustic Cues | Capoferri Davide | https://www.politesi.polimi.it/handle/10589/164950 |
| Audio processing | Sarti, Comanducci | Audio frame reconstruction from incomplete observations using Deep Learning techniques | Schils Minh Cédric | https://matheo.uliege.be/handle/2268.2/10138 |
| Audio processing | Sarti, Bernardini | Wave Digital Modeling and Simulation of Nonlinear Electromagnetic Circuits | Giampiccolo Riccardo | https://www.politesi.polimi.it/handle/10589/153994 |
| Audio processing | Sarti, Bernardini | Antiderivative Antialiasing in Nonlinear Wave Digital Filters | Albertini Davide | https://www.politesi.polimi.it/handle/10589/152934 |
| Audio processing | Sarti, Bernardini | Wave Digital Implementation of Nonlinear Audio Circuits based on the Scattering Iterative Method | Proverbio Alessandro | https://www.politesi.polimi.it/handle/10589/152323 |
| Audio processing | Antonacci | A system for super resolution vibrometric analysis through convolutional neural networks | Campagnoli Chiara | https://www.politesi.polimi.it/handle/10589/152613 |
| Audio processing | Antonacci | Development 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 tonewood | Villa Luca | https://www.politesi.polimi.it/handle/10589/150531 |
| Audio processing | Bestagini | DNN based post-filtering for quality improvement of AMR-WB decoded speech | Gupta Kishan | https://www.politesi.polimi.it/handle/10589/151000 |
| Audio processing | Sarti | Studio sull'implementazione degli algoritmi per il musical instruments ed il sound reinforcement basato su un processore multicore | Aretino Michele | https://www.politesi.polimi.it/handle/10589/139079 |
| Audio processing | Sarti, Bernardini | Modeling nonlinear 3-terminal devices in the wave digital domain | Vergani Alessio Emanuele | https://www.politesi.polimi.it/handle/10589/133184 |
| Forensics | Bestagini | Convolutional and recurrent neural networks for video tampering detection and localization | Cannas Edoardo Daniele | https://www.politesi.polimi.it/handle/10589/149900 |
| Forensics | Bestagini | A study on Bagging-Voronoi algorithm for tampering localization | Cereghetti Corinne Elena | https://www.politesi.polimi.it/handle/10589/141725 |
| Forensics | Bestagini | JPEG-based forensics through convolutional neural networks | Bonettini Nicolò | https://www.politesi.polimi.it/handle/10589/133727 |
| Forensics | Bestagini | Analysis of different footprints for JPEG compression detection | Chen Ke | https://www.politesi.polimi.it/handle/10589/132721 |
| Geophysics | Bestagini | Landmine detection on GPR data employing convolutional autoencoder | Testa Giuseppe | https://www.politesi.polimi.it/handle/10589/142106 |
| Image and video | Marcon, Paracchini | A novel tomographic approach for an early detection of multiple myeloma progression | Andrea Leggio | |
| Image and video | Marcon, Paracchini | Limited angle computed tomography reconstruction with deep learning enhancement | Erbol Kasenov, Girolamo Gerace | |
| Image and video | Marcon | Upper body postural assessment for common dentistry visual aids | Trotta Emilio | https://www.politesi.polimi.it/handle/10589/145563 |
| Image and video | Tubaro | Real-time tracking of electrode during deep-brain surgery | Dilauro Valerio | https://www.politesi.polimi.it/handle/10589/144685 |
| Image and video | Marcon | Analytical estimation of the error on the radius of industrial pipes | Lazzarin Sara | https://www.politesi.polimi.it/handle/10589/144394 |
| Image and video | Marcon | 3D reconstruction from stereo video acquired from odontoiatric microscope | Spatafora Leonardo | https://www.politesi.polimi.it/handle/10589/143780 |
| Image and video | Marcon | Denoising and classification of hyperspectral X-ray images for food quality assessment | Re Marco | https://www.politesi.polimi.it/handle/10589/142922 |
| Image and video | Marcon | A computer vision approach for assessment of dental bracket removal | Behnami Arezoo | https://www.politesi.polimi.it/handle/10589/142362 |
| Image and video | Marcon | Sistema per il rilevamento automatico di contaminanti alimentari basato su immagini iperspettrali | Ramoni Francesco | https://www.politesi.polimi.it/handle/10589/135891 |
| Image and video | Marcon | Postural assessment in dentistry by computer vision | Pignatelli Nicola | https://www.politesi.polimi.it/handle/10589/135030 |
| Multimedia forensics | Bestagini, Mandelli | A Multi-Modal Approach to Forensic Audio-Visual Device Identification | Davide Dal Cortivo | https://www.politesi.polimi.it/handle/10589/175593 |
| Music informatics | Sarti, Bernardini, Borrelli, Mezza | Estimating Harmonic Complexity of Chord Sequences using Transformer Networks | Cecilia Morato | |
| Music informatics | Zanoni, Comanducci | Modeling Harmonic Complexity in Automatic Music Generation using Conditional Variational Autoencoders | Davide Gioiosa | |
| Music informatics | Sarti, Borrelli, Comanducci | Cellular music : a novel music-generation platform based on an evolutionary paradigm | Matteo Manzolini | https://www.politesi.polimi.it/handle/10589/167291 |
| Music informatics | Sarti, Borrelli | Music emotion detection. A framework based on electrodermal activities. | Gioele Pozzi | https://www.politesi.polimi.it/handle/10589/152931 |
| Music informatics | Sarti, Comanducci | Techniques for mitigating the impact of latency in Networked Music Performance (NMP) through adaptive metronomes | Battello Riccardo | https://www.politesi.polimi.it/handle/10589/152923 |
| Music information retrieval | Sarti | Musical instrument recognition: a transfer learning approach | Molgora Andrea | https://www.politesi.polimi.it/handle/10589/147383 |
| Music information retrieval | Sarti | Unsupervised domain adaptation for deep learning based acoustic scene classification | Mezza Alessandro Ilic | https://www.politesi.polimi.it/handle/10589/145573 |
| Music information retrieval | Antonacci | An investigation of piano transcription algorithm for jazz music | Marzorati Giorgio | https://www.politesi.polimi.it/handle/10589/144745 |
| Music information retrieval | Sarti | Automatic playlist generation using recurrent neural network | Irene Rosilde Tatiana | https://www.politesi.polimi.it/handle/10589/142101 |
| Music information retrieval | Sarti | A personalized metric for music similarity using Siamese deep neural networks | Sala Federico | https://www.politesi.polimi.it/handle/10589/139078 |
| Music information retrieval | Sarti | Learning a personalized similarity metric for musical content | Carloni Luca | https://www.politesi.polimi.it/handle/10589/139076 |
| Music information retrieval | Sarti | Beat tracking using recurrent neural network : a transfer learning approach | Fiocchi Davide | https://www.politesi.polimi.it/handle/10589/139073 |
| Music information retrieval | Sarti | Python-based framework for managing a base of complex data for music information retrieval | Avocone Giuseppe | https://www.politesi.polimi.it/handle/10589/138449 |
| Music information retrieval | Sarti | Individual semantic modeling for music information retrieval | Ansidei Pietro | https://www.politesi.polimi.it/handle/10589/137160 |
| Music information retrieval | Sarti | Chord sequences : evaluating the effect of complexity on preference | Foscarin Francesco | https://www.politesi.polimi.it/handle/10589/136448 |
| Music information retrieval | Sarti | Audio features compensation based on coding bitrate | Tavella Maria Stella | https://www.politesi.polimi.it/handle/10589/134607 |
| Musical Acoustics | Antonacci | Modal analysis and optimization of the top plate of string instruments through a parametric control of their shape | Salvi Davide | https://www.politesi.polimi.it/handle/10589/166557 |
| Musical Acoustics | Antonacci, Pezzoli, Malvermi | An approach for Near-field Acoustic Holography based on Convolutional Autoencoders | Olivieri Marco | https://www.politesi.polimi.it/handle/10589/167039 |
| Space-time audio | Antonacci, Borra | A parametric approach to virtual miking with distributed microphone arrays | Marco Langè | |
| Space-time audio | Antonacci, Pezzoli, Borra, Bernardini | A Deep Prior Approach to Room Impulse Response Interpolation | Davide Perini | https://www.politesi.polimi.it/handle/10589/175583 |
| Space-time audio | Antonacci, Comanducci | Interpreting Deep Neural Networks Models for Acoustic Source Localization using Layer-wise Relevance Propagation | Alessandro Montali | https://www.politesi.polimi.it/handle/10589/169239 |
| Space-time audio | Antonacci, Borra, Bernardini | Analysis of Uniform Linear Arrays of Differential Microphones | Bertuletti Ivan | https://www.politesi.polimi.it/handle/10589/154604 |
| Space-time audio | Sarti | A geometrical method of 3D sound spatialization for virtual reality applications | Iamele Jacopo | https://www.politesi.polimi.it/handle/10589/143770 |
| Space-time audio | Antonacci | Convolutional neural networks applied to space-time audio processing applications | Comanducci Luca | https://www.politesi.polimi.it/handle/10589/139077 |
| Space-time audio | Canclini | Denoising in the spherical harmonic domain of sound scenes acquired by compact arrays | Borrelli Clara | https://www.politesi.polimi.it/handle/10589/139075 |
| Space-time audio | Antonacci | Simulazione di sistemi complessi. Case study : l'altoparlante a tromba | Moscara Francesco | https://www.politesi.polimi.it/handle/10589/139074 |
| Space-time audio | Sarti, Bernardini | Steerable differential microphone arrays | Lovatello Jacopo | https://www.politesi.polimi.it/handle/10589/139072 |
| Space-time audio | Antonacci | A plenacoustic approach to sound scene manipulation | Picetti Francesco | https://www.politesi.polimi.it/handle/10589/138430 |
| Space-time audio | Antonacci | Reconstruction of the soundfield in arbitrary locations using the distributed ray space transform | Pezzoli Mirco | https://www.politesi.polimi.it/handle/10589/136447 |
| Space-time audio | Sarti | A method for HRTF personalization : weighted sparse representation synthesis of HRTFs | Zhu Mo | https://www.politesi.polimi.it/handle/10589/135952 |
| Space-time audio | Antonacci | Robust parametric spatial audio processing using beamforming techniques | Milano Guendalina | https://www.politesi.polimi.it/handle/10589/134609 |
| Space-time audio | Antonacci | Estimation of singing voice quality through microphone in air and contact microphone | Landini Roberta | https://www.politesi.polimi.it/handle/10589/134604 |
| Musical Acoustics | Antonacci, Malvermi | Mechanical parameter estimation for vibrometric analysis and development of a low-cost platform for violin making | Federico Simeon | https://www.politesi.polimi.it/handle/10589/170995 |
| Space-time audio | Antonacci, Comanducci | 3D audio with irregular microphone setups using deep learning | Davide Mori | https://www.politesi.polimi.it/handle/10589/175608 |
| Space-time audio | Antonacci, Comanducci | Personalized Sound Zone Generation using Deep Learning | Roberto Alessandri | https://www.politesi.polimi.it/handle/10589/203852 |