| Analysis of intonation in unison choir singing H Cuesta, E Gómez Gutiérrez, A Martorell Domínguez, F Loáiciga | 55 | 2018 |
| Deep learning based source separation applied to choir ensembles D Petermann, P Chandna, H Cuesta, J Bonada, E Gómez arXiv preprint arXiv:2008.07645, 2020 | 34 | 2020 |
| Dagstuhl ChoirSet: A Multitrack Dataset for MIR Research on Choral Singing. S Rosenzweig, H Cuesta, C Weiß, F Scherbaum, E Gómez, M Müller Trans. Int. Soc. Music. Inf. Retr. 3 (1), 98-110, 2020 | 34 | 2020 |
| Multiple f0 estimation in vocal ensembles using convolutional neural networks H Cuesta, B McFee, E Gómez arXiv preprint arXiv:2009.04172, 2020 | 33 | 2020 |
| Deep learning for singing processing: Achievements, challenges and impact on singers and listeners E Gómez, M Blaauw, J Bonada, P Chandna, H Cuesta arXiv preprint arXiv:1807.03046, 2018 | 22 | 2018 |
| Cough-based COVID-19 detection with contextual attention convolutional neural networks and gender information A Mallol-Ragolta, H Cuesta, E Gómez, BW Schuller | 17 | 2021 |
| Multi-type outer product-based fusion of respiratory sounds for detecting COVID-19 A Mallol Ragolta, H Cuesta, E Gómez Gutiérrez, B Schuller Ko H, Hansen JHL. Interspeech 2022; 2022 Sep 18-22; Incheon, Korea.[Baixas …, 2022 | 10 | 2022 |
| A deep-learning based framework for source separation, analysis, and synthesis of choral ensembles P Chandna, H Cuesta, D Petermann, E Gómez Frontiers in Signal Processing 2, 808594, 2022 | 8 | 2022 |
| A framework for multi-f0 modeling in SATB choir recordings H Cuesta, E Gómez, P Chandna arXiv preprint arXiv:1904.05086, 2019 | 7 | 2019 |
| Data-driven pitch content description of choral singing recordings H Cuesta Universitat Pompeu Fabra, 2022 | 6 | 2022 |
| A deep learning based analysis-synthesis framework for unison singing P Chandna, H Cuesta, E Gómez arXiv preprint arXiv:2009.09875, 2020 | 5 | 2020 |
| Automatic transcription of Flamenco guitar falsetas S Rodríguez, E Gómez Gutiérrez, H Cuesta Holzapfel A, Pikrakis A, editors. Proceedings of the 8th International …, 2018 | 4 | 2018 |
| Can MusicGen Create Training Data for MIR Tasks? N Kroher, H Cuesta, A Pikrakis arXiv preprint arXiv:2311.09094, 2023 | 3 | 2023 |
| Voice assignment in vocal quartets using deep learning models based on pitch salience H Cuesta, E Gómez Gutiérrez Transactions of the International Society for Music Information Retrieval …, 2022 | 3 | 2022 |
| Choir Singers Pilot–An online platform for choir singers practice M Gover, Á Sarasúa, H Parra, J Janer, O Mayor, H Cuesta, MP Pascual, ... Proceedings of the Web Audio Conference (WAC), 2021 | 2 | 2021 |
| EIHW-MTG: Second DiCOVA Challenge System Report A Mallol-Ragolta, H Cuesta, E Gómez, BW Schuller arXiv preprint arXiv:2110.09239, 2021 | 1 | 2021 |
| EIHW-MTG DiCOVA 2021 Challenge System Report A Mallol-Ragolta, H Cuesta, E Gómez, BW Schuller arXiv preprint arXiv:2110.06543, 2021 | 1 | 2021 |
| Audio-based Music Retrieval E Gómez, H Cuesta, A Gkiokas, J Gómez-Cañón, L Porcaro, F Yesiler Information Retrieval: Advanced Topics and Techniques, 539-575, 2024 | | 2024 |
| DAACI-VoDAn: Improving Vocal Detection with New Data and Methods H Cuesta, N Kroher, A Pikrakis, S Djordjevic 2023 31st European Signal Processing Conference (EUSIPCO), 136-140, 2023 | | 2023 |
| Automatic structure detection and visualization in symphonic music H Cuesta | | 2015 |
