7 citations · 16 across the 6 of their papers we have counts for
7 papers
EIHW-MTG: Second DiCOVA Challenge System Report
Adria Mallol-Ragolta, Helena Cuesta, Emilia Gómez +1
This work presents an outer product-based approach to fuse the embedded representations generated from the spectrograms of cough, breath, and speech samples for the automatic detec…
EIHW-MTG DiCOVA 2021 Challenge System Report
Adria Mallol-Ragolta, Helena Cuesta, Emilia Gómez +1
This paper aims to automatically detect COVID-19 patients by analysing the acoustic information embedded in coughs. COVID-19 affects the respiratory system, and, consequently, resp…
A Deep Learning Based Analysis-Synthesis Framework For Unison Singing
Pritish Chandna, Helena Cuesta, Emilia Gómez
Unison singing is the name given to an ensemble of singers simultaneously singing the same melody and lyrics. While each individual singer in a unison sings the same principle melo…
Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks
Helena Cuesta, Brian McFee, Emilia Gómez
This paper addresses the extraction of multiple F0 values from polyphonic and a cappella vocal performances using convolutional neural networks (CNNs). We address the major challen…
Deep Learning Based Source Separation Applied To Choir Ensembles
Darius Petermann, Pritish Chandna, Helena Cuesta +2
Choral singing is a widely practiced form of ensemble singing wherein a group of people sing simultaneously in polyphonic harmony. The most commonly practiced setting for choir ens…
A Framework for Multi-f0 Modeling in SATB Choir Recordings
Helena Cuesta, Emilia Gómez, Pritish Chandna
Fundamental frequency (f0) modeling is an important but relatively unexplored aspect of choir singing. Performance evaluation as well as auditory analysis of singing, whether indiv…