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N. Mitianoudis, and M. Davies, "Intelligent Audio Source Separation using Independent Component Analysis," Paper 5529, (2002 April.). doi: N. Mitianoudis, and M. Davies, "Intelligent Audio Source Separation using Independent Component Analysis," Paper 5529, (2002 April.). doi:
Abstract: The authors introduce the idea of performing it Intelligent ICA to focus on and separate a specific instrument, voice or sound source of interest. This is achieved by incorporating high-level probabilistic priors in the ICA model that characterise each instrument or voice. For instrument modelling, we experimented with various feature sets previously used for instrument or speaker recognition. Prior training of a Gaussian Mixture Model for each instrument was performed. The order of the feature vector, the number of gaussian mixtures and the training audio data length were kept to reasonably minimum levels.

@article{mitianoudis2002intelligent,
author={mitianoudis, nikolaos and davies, mike},
journal={journal of the audio engineering society},
title={intelligent audio source separation using independent component analysis},
year={2002},
volume={},
number={},
pages={},
doi={},
month={april},} @article{mitianoudis2002intelligent,
author={mitianoudis, nikolaos and davies, mike},
journal={journal of the audio engineering society},
title={intelligent audio source separation using independent component analysis},
year={2002},
volume={},
number={},
pages={},
doi={},
month={april},
abstract={the authors introduce the idea of performing it intelligent ica to focus on and separate a specific instrument, voice or sound source of interest. this is achieved by incorporating high-level probabilistic priors in the ica model that characterise each instrument or voice. for instrument modelling, we experimented with various feature sets previously used for instrument or speaker recognition. prior training of a gaussian mixture model for each instrument was performed. the order of the feature vector, the number of gaussian mixtures and the training audio data length were kept to reasonably minimum levels.},}

TY - paper
TI - Intelligent Audio Source Separation using Independent Component Analysis
SP - EP -
AU - Mitianoudis, Nikolaos
AU - Davies, Mike
PY - 2002
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - April 2002 TY - paper
TI - Intelligent Audio Source Separation using Independent Component Analysis
SP - EP -
AU - Mitianoudis, Nikolaos
AU - Davies, Mike
PY - 2002
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - April 2002
AB - The authors introduce the idea of performing it Intelligent ICA to focus on and separate a specific instrument, voice or sound source of interest. This is achieved by incorporating high-level probabilistic priors in the ICA model that characterise each instrument or voice. For instrument modelling, we experimented with various feature sets previously used for instrument or speaker recognition. Prior training of a Gaussian Mixture Model for each instrument was performed. The order of the feature vector, the number of gaussian mixtures and the training audio data length were kept to reasonably minimum levels.