Working from remote in Ecuador!
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The experience of working from remote in Ecuador! 
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publications
Solos: A Dataset for Audio-Visual Music Source Separation and Localization
Published in IEEE MMSP2020, 2020
Audiovisual Dataset of musicians playing different instruments. Openpose skeleton provided framewise
Recommended citation: Juan F. Montesinos, Olga Slizovskaia, Gloria Haro (2020). "Solos: A Dataset for Audio-Visual Music Source Separation and Localization" IEEE MMSP 2020 1. https://arxiv.org/pdf/2006.07931.pdf
Multi-channel U-Net for Music Source Separation
Published in IEEE MMSP 2020, 2020
Weighted losses applied to a Multi-channel U-Net
Recommended citation: Venkatesh Shenoy, Juan F. Montesinos, Gloria Haro, Emilia Gómez (2020). "Multi-channel U-Net for Music Source Separation." IEEE MMSP2020 1. https://arxiv.org/pdf/2003.10414.pdf
Estimating Individual A Cappella Voices in Music Videos with Singing Faces
Published in S&S CVPR21, 2021
Audiovisual singing voice separation
Recommended citation: Venkatesh S. Kadandale,Juan F. Montesinos, Gloria Haro (2021). "Estimating Individual A Cappella Voices in Music Videos with Singing FacesS6S CVPR21 https://sightsound.org/papers/2021/Venkatesh_Shenoy_Kadandale_Estimating_Individual_A_Cappella_Voices_in_Music_Videos_with_Singing_Faces.pdf
A cappella: Audio-visual Singing Voice Separation
Published in BMVC 2021, 2021
Graph CNN for singing voice separation
Recommended citation: Juan F. Montesinos, Venkatesh S. Kadandale, Gloria Haro (2021). "A cappella: Audio-visual Singing Voice SeparationBMVC 21 https://arxiv.org/abs/2104.09946
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Calculus II
Undergraduate course, Pompeu Fabra University, DTIC, 2019
Source separation in musical videos via motion analysis
Degree's Dissertation, Pompeu Fabra University, DTIC, 2019
Repository
Resume EN: This project proposes a method for the task of audio source separation of a signal, based on the movements of the players related to that signal. The process is composed of three blocks. The first block, computes a frequential analysis of the original signal by Non- negative Matrix Factorization (NMF). The video processing block estimates the velocity signal of the movements of each player by two types of video segmentation: the first one is based on motion trajectories of the objects in the scene, while the second one, uses optical flow and Principal Component Analysis. The last processing block makes a cor- relation between the frequential information and the velocity signals, using four variation of a method based on NMF and Non-Negative Least Squares. Finally, some experiments show the efficacy of the different variants of the audio source separation method.