50 citations · 70 across the 9 of their papers we have counts for
11 papers
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…
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…
Interpreting deep urban sound classification using Layer-wise Relevance Propagation
Marco Colussi, Stavros Ntalampiras
After constructing a deep neural network for urban sound classification, this work focuses on the sensitive application of assisting drivers suffering from hearing loss. As such, c…
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…
One-shot learning for acoustic identification of bird species in non-stationary environments
Michelangelo Acconcjaioco, Stavros Ntalampiras
This work introduces the one-shot learning paradigm in the computational bioacoustics domain. Even though, most of the related literature assumes availability of data characterizin…
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…
Cross-modal variational inference for bijective signal-symbol translation
Axel Chemla--Romeu-Santos, Stavros Ntalampiras, Philippe Esling +2
Extraction of symbolic information from signals is an active field of research enabling numerous applications especially in the Musical Information Retrieval domain. This complex t…
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…