Gaussian Framework for Interference Reduction in Live Recordings
×
Cite This
Citation & Abstract
D. Di Carlo, K. Déguernel, and A. Liutkus, "Gaussian Framework for Interference Reduction in Live Recordings," Paper 1-1, (2017 June.). doi:
D. Di Carlo, K. Déguernel, and A. Liutkus, "Gaussian Framework for Interference Reduction in Live Recordings," Paper 1-1, (2017 June.). doi:
Abstract: In live multitrack recordings, each voice is usually captured by dedicated close microphones. Unfortunately, it is also captured in practice by other microphones intended for other sources, leading to so-called “interferences”. Reducing this leakage is desirable because it opens new perspectives for the engineering of live recordings. Hence, it has been the topic of recent research in audio processing. In this paper, we show how a Gaussian probabilistic framework may be set up for obtaining good isolation of the target sources. Doing so, we extend several state-of-the art methods by fixing some heuristic parts of their algorithms. As we show in a perceptual evaluation on real-world multitrack live recordings, the resulting principled techniques yield improved quality.
@article{di carlo2017gaussian,
author={di carlo, diego and déguernel, ken and liutkus, antoine},
journal={journal of the audio engineering society},
title={gaussian framework for interference reduction in live recordings},
year={2017},
volume={},
number={},
pages={},
doi={},
month={june},}
@article{di carlo2017gaussian,
author={di carlo, diego and déguernel, ken and liutkus, antoine},
journal={journal of the audio engineering society},
title={gaussian framework for interference reduction in live recordings},
year={2017},
volume={},
number={},
pages={},
doi={},
month={june},
abstract={in live multitrack recordings, each voice is usually captured by dedicated close microphones. unfortunately, it is also captured in practice by other microphones intended for other sources, leading to so-called “interferences”. reducing this leakage is desirable because it opens new perspectives for the engineering of live recordings. hence, it has been the topic of recent research in audio processing. in this paper, we show how a gaussian probabilistic framework may be set up for obtaining good isolation of the target sources. doing so, we extend several state-of-the art methods by fixing some heuristic parts of their algorithms. as we show in a perceptual evaluation on real-world multitrack live recordings, the resulting principled techniques yield improved quality.},}
TY - paper
TI - Gaussian Framework for Interference Reduction in Live Recordings
SP -
EP -
AU - Di Carlo, Diego
AU - Déguernel, Ken
AU - Liutkus, Antoine
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - June 2017
TY - paper
TI - Gaussian Framework for Interference Reduction in Live Recordings
SP -
EP -
AU - Di Carlo, Diego
AU - Déguernel, Ken
AU - Liutkus, Antoine
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - June 2017
AB - In live multitrack recordings, each voice is usually captured by dedicated close microphones. Unfortunately, it is also captured in practice by other microphones intended for other sources, leading to so-called “interferences”. Reducing this leakage is desirable because it opens new perspectives for the engineering of live recordings. Hence, it has been the topic of recent research in audio processing. In this paper, we show how a Gaussian probabilistic framework may be set up for obtaining good isolation of the target sources. Doing so, we extend several state-of-the art methods by fixing some heuristic parts of their algorithms. As we show in a perceptual evaluation on real-world multitrack live recordings, the resulting principled techniques yield improved quality.
In live multitrack recordings, each voice is usually captured by dedicated close microphones. Unfortunately, it is also captured in practice by other microphones intended for other sources, leading to so-called “interferences”. Reducing this leakage is desirable because it opens new perspectives for the engineering of live recordings. Hence, it has been the topic of recent research in audio processing. In this paper, we show how a Gaussian probabilistic framework may be set up for obtaining good isolation of the target sources. Doing so, we extend several state-of-the art methods by fixing some heuristic parts of their algorithms. As we show in a perceptual evaluation on real-world multitrack live recordings, the resulting principled techniques yield improved quality.
Authors:
Di Carlo, Diego; Déguernel, Ken; Liutkus, Antoine
Affiliations:
INRIA, Villers-les-Nancy, France; STMS Lab IRCAM/CNRS/UPMC, Paris, France(See document for exact affiliation information.)
AES Conference:
2017 AES International Conference on Semantic Audio (June 2017)
Paper Number:
1-1
Publication Date:
June 13, 2017Import into BibTeX
Subject:
Audio Source Separation
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=18751