50 citations · 77 across the 14 of their papers we have counts for
16 papers
Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network with Domain-Adversarial Training
Ali Tabaraei, Federico Simonetta, Stavros Ntalampiras
Automatic depression detection with deep learning has shown promise but often suffers from limited generalization due to domain shift arising from inter-speaker variability. To add…
Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey
Federico Simonetta
This paper presents the first comprehensive systematic review of literature on style-based composer identification and authorship attribution in symbolic music scores. Addressing t…
Optical Music Recognition in Manuscripts from the Ricordi Archive
Federico Simonetta, Rishav Mondal, Luca Andrea Ludovico +1
The Ricordi archive, a prestigious collection of significant musical manuscripts from renowned opera composers such as Donizetti, Verdi and Puccini, has been digitized. This proces…
Joint Learning of Emotions in Music and Generalized Sounds
Federico Simonetta, Francesca Certo, Stavros Ntalampiras
In this study, we aim to determine if generalized sounds and music can share a common emotional space, improving predictions of emotion in terms of arousal and valence. We propose…
A Systematic Evaluation of Adversarial Attacks against Speech Emotion Recognition Models
Nicolas Facchinetti, Federico Simonetta, Stavros Ntalampiras
Speech emotion recognition (SER) is constantly gaining attention in recent years due to its potential applications in diverse fields and thanks to the possibility offered by deep l…
Optimizing Feature Extraction for Symbolic Music
Federico Simonetta, Ana Llorens, Martín Serrano +2
This paper presents a comprehensive investigation of existing feature extraction tools for symbolic music and contrasts their performance to determine the set of features that best…
musif: a Python package for symbolic music feature extraction
Ana Llorens, Federico Simonetta, Martín Serrano +1
In this work, we introduce musif, a Python package that facilitates the automatic extraction of features from symbolic music scores. The package includes the implementation of a la…
Deep Feature Learning for Medical Acoustics
Alessandro Maria Poirè, Federico Simonetta, Stavros Ntalampiras
The purpose of this paper is to compare different learnable frontends in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbea…
Variational Autoencoders for Anomaly Detection in Respiratory Sounds
Michele Cozzatti, Federico Simonetta, Stavros Ntalampiras
This paper proposes a weakly-supervised machine learning-based approach aiming at a tool to alert patients about possible respiratory diseases. Various types of pathologies may aff…
Music Interpretation Analysis. A Multimodal Approach To Score-Informed Resynthesis of Piano Recordings
Federico Simonetta
This Thesis discusses the development of technologies for the automatic resynthesis of music recordings using digital synthesizers. First, the main issue is identified in the under…
A Perceptual Measure for Evaluating the Resynthesis of Automatic Music Transcriptions
Federico Simonetta, Federico Avanzini, Stavros Ntalampiras
This study focuses on the perception of music performances when contextual factors, such as room acoustics and instrument, change. We propose to distinguish the concept of "perform…
Acoustics-specific Piano Velocity Estimation
Federico Simonetta, Stavros Ntalampiras, Federico Avanzini
Motivated by the state-of-art psychological research, we note that a piano performance transcribed with existing Automatic Music Transcription (AMT) methods cannot be successfully…
Audio-to-Score Alignment Using Deep Automatic Music Transcription
Federico Simonetta, Stavros Ntalampiras, Federico Avanzini
Audio-to-score alignment (A2SA) is a multimodal task consisting in the alignment of audio signals to music scores. Recent literature confirms the benefits of Automatic Music Transc…
ASMD: an automatic framework for compiling multimodal datasets with audio and scores
Federico Simonetta, Stavros Ntalampiras, Federico Avanzini
This paper describes an open-source Python framework for handling datasets for music processing tasks, built with the aim of improving the reproducibility of research projects in m…
A Convolutional Approach to Melody Line Identification in Symbolic Scores
Federico Simonetta, Carlos Cancino-Chacón, Stavros Ntalampiras +1
In many musical traditions, the melody line is of primary significance in a piece. Human listeners can readily distinguish melodies from accompaniment; however, making this distinc…
Multimodal music information processing and retrieval: survey and future challenges
Federico Simonetta, Stavros Ntalampiras, Federico Avanzini
Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modaliti…