Helena Cuesta

Research Scientist

< music + audio + ML + signal processing >

helenacuesta.hcm@gmail.com

Publications

Ph.D. Dissertation:

Helena Cuesta (2022). Data-driven Pitch Content Description of Choral Singing Recordings. PhD thesis, Universitat Pompeu Fabra. http://hdl.handle.net/10803/673924

Journal article / Book chapter :

  • Helena Cuesta and Emilia Gómez (2022). Voice Assignment in Vocal Quartets using DeepLearning Models based on Pitch Salience. Transactions of the International Society for Music Information Retrieval (TISMIR), 5 (1): 99-112. Paper.
  • Pritish Chandna, Helena Cuesta, Darius Petermann, Emilia Gómez (2022). A Deep-Learning Based Framework for Source Separation, Analysis, and Synthesis of Choral Ensembles. Frontiers in Signal Processing, 2, 808594. Paper.
  • Sebastian Rosenzweig, Helena Cuesta, Christof Weiß, Frank Scherbaum, Emilia Gómez, & Meinard Müller (2020). Dagstuhl ChoirSet: A Multitrack Dataset for MIR Research on Choral Singing. In Transactions of the International Society for Music Information Retrieval, 3(1), 98–110. DOI: http://doi.org/10.5334/tismir.48. Paper.
  • Emilia Gómez, Helena Cuesta, Aggelos Gkiokas, Juan S. Gómez-Cañón, Lorenzo Porcaro & Furkan Yesiler. Audio-based Music Information Retrieval: from knowledge-driven to data-driven design. In Alonso, O. & Baeza-Yates, R. (Eds.). Advanced Topics for Information Retrieval. ACM Press. Forthcoming, 2024.

Conference/workshop papers (first authored papers, chronologically):

  • Helena Cuesta, Nadine Kroher, Aggelos Pikrakis, Stojan Djordjevic. DAACI-VoDAn: Improving Vocal Detection with New Data and Methods. In Proceedings of the Euopean Signal Processing Conference (EUSIPCO). Helsinki, Finland. September 2023. Paper.
  • Helena Cuesta, Brian McFee, & Emilia Gómez (2020). Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR). Montreal, Canada (virtual), pp. 302-309. Paper.
  • Helena Cuesta, Emilia Gómez, & Pritish Chandna (2019). A Framework for multi-f0 modeling in SATB choir recordings. In Proceedings of the Sound and Music Computing (SMC) Conference. Málaga, Spain. Paper.
  • Helena Cuesta, Emilia Gómez, Agustín Martorell & Felipe Loáiciga (2018). Analysis of Intonation in Unison Choir Singing. In ​Proceedings 15th International Conference on Music Perception and Cognition (ICMPC) and the 10th Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM). Graz, Austria. Paper.
  • Helena Cuesta and Emilia Gómez (2018). Measuring Interdependence in Unison Choral Singing. ​Presented at the Late-breaking demo session of the 19th International Society for Music Information Retrieval Conference (ISMIR). Paris, France.

Conference/workshop papers (co-authored, chronologically):

  • Adrià Mallol, Helena Cuesta, Emilia Gómez & Björn Schuller (2021). Cough-based COVID-19 Detection with Contextual Attention Convolutional Neural Networks and Gender Information. Accepted at Interspeech 2021. Brno, Czechia.
  • Matan Gover, Álvaro Sarasua, Héctor Parra, Jordi Janer, Óscar Mayor, Helena Cuesta, Aggelos Gkiokas, Maria Pilar Pascual, Emilia Gómez (2021). Choir Singers Platform -- An online platform for choir singers practice. To appear in Proceedings of the Web Audio Conference (WAC). Barcelona, Spain.
  • Pritish Chandna, Helena Cuesta & Emilia Gómez (2020). A Deep Learning Based Analysis-Synthesis Framework For Unison Singing. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR). Montreal, Canada (virtual), pp.
  • Darius Petermann, Pritish Chandna, Helena Cuesta, Jordi Bonada & Emilia Gómez (2020). Deep Learning Based Source Separation Applied To Choir Ensembles. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR). Montreal, Canada (virtual), pp.733-739.
  • Emilia Gómez, Merlijn Blaauw, Jordi Bonada, Pritish Chandna, & Helena Cuesta (2018). Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners. In 2018 Joint Workshop on Machine Learning for Music. 1 ed.
  • Sonia Rodríguez, Emilia Gómez, and Helena Cuesta (2018). Automatic Transcription of Flamenco Guitar Falsetas. In ​Proceedings Folk Music Analysis Workshop ​(​FMA). Thessaloniki, Greece.
Plain Academic