3 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
Learning Input-Channel Permutation Equivariance for Multi-Channel Source Separation: Reducing Bleeding in Small Music Ensembles
Ruchi Pandey, Jaime Garcia-Martinez, Pablo Cabanas-Molero +5
Microphone bleed is a persistent challenge in small ensembles and orchestral recordings, where close microphones intended for individual instruments also capture leakage from nearb…
The Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval
Jaime Garcia-Martinez, David Diaz-Guerra, John Anderson +5
This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source separation and related MIR tasks within th…
SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation
Jaime Garcia-Martinez, David Diaz-Guerra, Archontis Politis +3
Recent advancements in music source separation have significantly progressed, particularly in isolating vocals, drums, and bass elements from mixed tracks. These developments owe m…
Multichannel Singing Voice Separation by Deep Neural Network Informed DOA Constrained CNMF
Antonio J. Muñoz-Montoro, Julio J. Carabias-Orti, Archontis Politis +1
This work addresses the problem of multichannel source separation combining two powerful approaches, multichannel spectral factorization with recent monophonic deep-learning (DL) b…