Monoaural Audio Source Separation Using Deep Convolutional Neural Networks. - Pritish Chandna; Marius Miron; Jordi Janer; Emilia Gómez (LVA/ICA, 2017)

Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners. - Emilia Gómez; Merlijn Blaauw; Jordi Bonada; Pritish Chandna; Helena Cuesta (CoRR, 2018)

WGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN. - Pritish Chandna; Merlijn Blaauw; Jordi Bonada; Emilia Gómez (EUSIPCO, 2019)

A Vocoder Based Method for Singing Voice Extraction. - Pritish Chandna; Merlijn Blaauw; Jordi Bonada; Emilia Gómez (ICASSP, 2019)

A Framework for Multi-f0 Modeling in SATB Choir Recordings. - Helena Cuesta; Emilia Gómez; Pritish Chandna (CoRR, 2019)

Content Based Singing Voice Extraction from a Musical Mixture. - Pritish Chandna; Merlijn Blaauw; Jordi Bonada; Emilia Gómez (ICASSP, 2020)

Neural Percussive Synthesis Parameterised by High-Level Timbral Features. - António Ramires; Pritish Chandna; Xavier Favory; Emilia Gómez; Xavier Serra (ICASSP, 2020)

A Deep Learning Based Analysis-Synthesis Framework For Unison Singing. - Pritish Chandna; Helena Cuesta; Emilia Gómez (ISMIR, 2020)

Deep Learning Based Source Separation Applied To Choir Ensembles. - Darius Petermann; Pritish Chandna; Helena Cuesta; Jordi Bonada; Emilia Gómez (ISMIR, 2020)

Loopnet: Musical Loop Synthesis Conditioned on Intuitive Musical Parameters. - Pritish Chandna; António Ramires; Xavier Serra; Emilia Gómez (ICASSP, 2021)

Neural networks for singing voice extraction in monaural polyphonic music signals. - Pritish Chandna (2021)

Voice conversion with limited data and limitless data augmentations. - Olga Slizovskaia; Jordi Janer; Pritish Chandna; Oscar Mayor (CoRR, 2022)