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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
A cappella: Audio-visual Singing Voice Separation
Published in Under review, 2021
Audiovisual model and dataset for singing voice separation
Recommended citation: Juan F. Montesinos, Venkatesh S. Kadandale, Gloria Haro (2021). "A cappella: Audio-visual Singing Voice Separation" https://arxiv.org/pdf/2006.3708242.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
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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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.