Search Results for author: Danilo Comminiello

Found 30 papers, 15 papers with code

SyncFusion: Multimodal Onset-synchronized Video-to-Audio Foley Synthesis

no code implementations23 Oct 2023 Marco Comunità, Riccardo F. Gramaccioni, Emilian Postolache, Emanuele Rodolà, Danilo Comminiello, Joshua D. Reiss

Sound design involves creatively selecting, recording, and editing sound effects for various media like cinema, video games, and virtual/augmented reality.

Generalizing Medical Image Representations via Quaternion Wavelet Networks

1 code implementation16 Oct 2023 Luigi Sigillo, Eleonora Grassucci, Aurelio Uncini, Danilo Comminiello

The proposed quaternion wavelet network (QUAVE) can be easily integrated with any pre-existing medical image analysis or synthesis task, and it can be involved with real, quaternion, or hypercomplex-valued models, generalizing their adoption to single-channel data.

Dual Quaternion Rotational and Translational Equivariance in 3D Rigid Motion Modelling

no code implementations11 Oct 2023 Guilherme Vieira, Eleonora Grassucci, Marcos Eduardo Valle, Danilo Comminiello

To overcome these limitations, we employ a dual quaternion representation of rigid motions in the 3D space that jointly describes rotations and translations of point sets, processing each of the points as a single entity.

Human Pose Forecasting

Attention-Map Augmentation for Hypercomplex Breast Cancer Classification

no code implementations11 Oct 2023 Eleonora Lopez, Filippo Betello, Federico Carmignani, Eleonora Grassucci, Danilo Comminiello

In this step, a parameterized hypercomplex neural network (PHNN) is employed to perform breast cancer classification.


PHYDI: Initializing Parameterized Hypercomplex Neural Networks as Identity Functions

1 code implementation11 Oct 2023 Matteo Mancanelli, Eleonora Grassucci, Aurelio Uncini, Danilo Comminiello

Neural models based on hypercomplex algebra systems are growing and prolificating for a plethora of applications, ranging from computer vision to natural language processing.

Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals

1 code implementation11 Oct 2023 Eleonora Lopez, Eleonora Chiarantano, Eleonora Grassucci, Danilo Comminiello

Multimodal emotion recognition from physiological signals is receiving an increasing amount of attention due to the impossibility to control them at will unlike behavioral reactions, thus providing more reliable information.

EEG Electroencephalogram (EEG) +1

Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview

no code implementations5 Sep 2023 Eleonora Grassucci, Yuki Mitsufuji, Ping Zhang, Danilo Comminiello

Semantic communication is poised to play a pivotal role in shaping the landscape of future AI-driven communication systems.

Generative Semantic Communication: Diffusion Models Beyond Bit Recovery

1 code implementation7 Jun 2023 Eleonora Grassucci, Sergio Barbarossa, Danilo Comminiello

We prove, through an in-depth assessment of multiple scenarios, that our method outperforms existing solutions in generating high-quality images with preserved semantic information even in cases where the received content is significantly degraded.

Hypercomplex Image-to-Image Translation

1 code implementation4 May 2022 Eleonora Grassucci, Luigi Sigillo, Aurelio Uncini, Danilo Comminiello

Image-to-image translation (I2I) aims at transferring the content representation from an input domain to an output one, bouncing along different target domains.

Image-to-Image Translation Translation

Hypercomplex Neural Architectures for Multi-View Breast Cancer Classification

2 code implementations12 Apr 2022 Eleonora Lopez, Eleonora Grassucci, Martina Valleriani, Danilo Comminiello

The proposed methods are able to handle the information of a patient altogether without breaking the multi-view nature of the exam.


Learning Speech Emotion Representations in the Quaternion Domain

1 code implementation5 Apr 2022 Eric Guizzo, Tillman Weyde, Simone Scardapane, Danilo Comminiello

On the one hand, the classifier permits to optimize each latent axis of the embeddings for the classification of a specific emotion-related characteristic: valence, arousal, dominance and overall emotion.

Speech Emotion Recognition

Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation

1 code implementation4 Apr 2022 Eleonora Grassucci, Gioia Mancini, Christian Brignone, Aurelio Uncini, Danilo Comminiello

We show that our dual quaternion SELD model with temporal convolution blocks (DualQSELD-TCN) achieves better results with respect to real and quaternion-valued baselines thanks to our augmented representation of the sound field.

Sound Event Localization and Detection

L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment

1 code implementation21 Feb 2022 Eric Guizzo, Christian Marinoni, Marco Pennese, Xinlei Ren, Xiguang Zheng, Chen Zhang, Bruno Masiero, Aurelio Uncini, Danilo Comminiello

The L3DAS22 Challenge is aimed at encouraging the development of machine learning strategies for 3D speech enhancement and 3D sound localization and detection in office-like environments.

Sound Event Localization and Detection Speech Enhancement

PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions

4 code implementations8 Oct 2021 Eleonora Grassucci, Aston Zhang, Danilo Comminiello

In this paper, we define the parameterization of hypercomplex convolutional layers and introduce the family of parameterized hypercomplex neural networks (PHNNs) that are lightweight and efficient large-scale models.

Sound Event Detection

A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

no code implementations19 Apr 2021 Danilo Comminiello, Alireza Nezamdoust, Simone Scardapane, Michele Scarpiniti, Amir Hussain, Aurelio Uncini

In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters.

Acoustic echo cancellation Domain Adaptation

Quaternion Generative Adversarial Networks

3 code implementations19 Apr 2021 Eleonora Grassucci, Edoardo Cicero, Danilo Comminiello

Latest Generative Adversarial Networks (GANs) are gathering outstanding results through a large-scale training, thus employing models composed of millions of parameters requiring extensive computational capabilities.

Image Generation

L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

1 code implementation12 Apr 2021 Eric Guizzo, Riccardo F. Gramaccioni, Saeid Jamili, Christian Marinoni, Edoardo Massaro, Claudia Medaglia, Giuseppe Nachira, Leonardo Nucciarelli, Ludovica Paglialunga, Marco Pennese, Sveva Pepe, Enrico Rocchi, Aurelio Uncini, Danilo Comminiello

The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD).

Audio Signal Processing BIG-bench Machine Learning +1

A Quaternion-Valued Variational Autoencoder

3 code implementations22 Oct 2020 Eleonora Grassucci, Danilo Comminiello, Aurelio Uncini

Deep probabilistic generative models have achieved incredible success in many fields of application.

Combined Sparse Regularization for Nonlinear Adaptive Filters

no code implementations24 Jul 2020 Danilo Comminiello, Michele Scarpiniti, Simone Scardapane, Luis A. Azpicueta-Ruiz, Aurelio Uncini

Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity.

A Multimodal Deep Network for the Reconstruction of T2W MR Images

no code implementations8 Aug 2019 Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini

In this paper, we present a deep learning method that is able to reconstruct subsampled MR images obtained by reducing the k-space data, while maintaining a high image quality that can be used to observe brain lesions.

Compressing deep quaternion neural networks with targeted regularization

no code implementations26 Jul 2019 Riccardo Vecchi, Simone Scardapane, Danilo Comminiello, Aurelio Uncini

To this end, we investigate two extensions of l1 and structured regularization to the quaternion domain.

Image Reconstruction

Widely Linear Kernels for Complex-Valued Kernel Activation Functions

no code implementations6 Feb 2019 Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello, Aurelio Uncini

Complex-valued neural networks (CVNNs) have been shown to be powerful nonlinear approximators when the input data can be properly modeled in the complex domain.

Image Classification Test

Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events

no code implementations17 Dec 2018 Danilo Comminiello, Marco Lella, Simone Scardapane, Aurelio Uncini

Learning from data in the quaternion domain enables us to exploit internal dependencies of 4D signals and treating them as a single entity.

Event Detection Sound Event Detection

Improving Graph Convolutional Networks with Non-Parametric Activation Functions

no code implementations26 Feb 2018 Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello, Aurelio Uncini

Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform inference on data that is associated to a graph structure, such as, e. g., citation networks or knowledge graphs.

Knowledge Graphs

Group Sparse Regularization for Deep Neural Networks

1 code implementation2 Jul 2016 Simone Scardapane, Danilo Comminiello, Amir Hussain, Aurelio Uncini

In this paper, we consider the joint task of simultaneously optimizing (i) the weights of a deep neural network, (ii) the number of neurons for each hidden layer, and (iii) the subset of active input features (i. e., feature selection).

feature selection Handwritten Digit Recognition

Effective Blind Source Separation Based on the Adam Algorithm

no code implementations25 May 2016 Michele Scarpiniti, Simone Scardapane, Danilo Comminiello, Raffaele Parisi, Aurelio Uncini

In this paper, we derive a modified InfoMax algorithm for the solution of Blind Signal Separation (BSS) problems by using advanced stochastic methods.

Stochastic Optimization

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