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P. Dalka, M. Dziubinski, and B. Kostek, "Comparison of Effectiveness of Musical Sound Separation Algorithms Employing Neural Networks," Paper 6310, (2004 October.). doi: P. Dalka, M. Dziubinski, and B. Kostek, "Comparison of Effectiveness of Musical Sound Separation Algorithms Employing Neural Networks," Paper 6310, (2004 October.). doi:
Abstract: In this paper several algorithms are presented, developed for musical sound separation. The proposed techniques for the decomposition of mixed sounds are based on the assumption that pitch of the sounds contained in the mix is known, i.e. inputs of the algorithms are pitch tracks of the signals contained in the mixture. The estimation process of phase and amplitude contours representing harmonic components is based on the limited number of inner product operations, performed on the signal with the use of complex exponentials matching pitch characteristics of the separated signals, and not on the discrete spectral representations calculated via DFT. In this paper examples of separation results are presented and each algorithm performance is analyzed. The effectiveness of separation algorithms consists in calculation of feature vectors (FVs) derived from musical sounds after the separation process is performed, and then in feeding them the Neural Network (NN) for automatic musical sound identification. The experimental results are shown and discussed. A comparison of effectiveness of all presented algorithms is also included, and conclusions are derived.

@article{dalka2004comparison,
author={dalka, piotr and dziubinski, marek and kostek, bozena},
journal={journal of the audio engineering society},
title={comparison of effectiveness of musical sound separation algorithms employing neural networks},
year={2004},
volume={},
number={},
pages={},
doi={},
month={october},} @article{dalka2004comparison,
author={dalka, piotr and dziubinski, marek and kostek, bozena},
journal={journal of the audio engineering society},
title={comparison of effectiveness of musical sound separation algorithms employing neural networks},
year={2004},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={in this paper several algorithms are presented, developed for musical sound separation. the proposed techniques for the decomposition of mixed sounds are based on the assumption that pitch of the sounds contained in the mix is known, i.e. inputs of the algorithms are pitch tracks of the signals contained in the mixture. the estimation process of phase and amplitude contours representing harmonic components is based on the limited number of inner product operations, performed on the signal with the use of complex exponentials matching pitch characteristics of the separated signals, and not on the discrete spectral representations calculated via dft. in this paper examples of separation results are presented and each algorithm performance is analyzed. the effectiveness of separation algorithms consists in calculation of feature vectors (fvs) derived from musical sounds after the separation process is performed, and then in feeding them the neural network (nn) for automatic musical sound identification. the experimental results are shown and discussed. a comparison of effectiveness of all presented algorithms is also included, and conclusions are derived.},}

TY - paper
TI - Comparison of Effectiveness of Musical Sound Separation Algorithms Employing Neural Networks
SP - EP -
AU - Dalka, Piotr
AU - Dziubinski, Marek
AU - Kostek, Bozena
PY - 2004
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2004 TY - paper
TI - Comparison of Effectiveness of Musical Sound Separation Algorithms Employing Neural Networks
SP - EP -
AU - Dalka, Piotr
AU - Dziubinski, Marek
AU - Kostek, Bozena
PY - 2004
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2004
AB - In this paper several algorithms are presented, developed for musical sound separation. The proposed techniques for the decomposition of mixed sounds are based on the assumption that pitch of the sounds contained in the mix is known, i.e. inputs of the algorithms are pitch tracks of the signals contained in the mixture. The estimation process of phase and amplitude contours representing harmonic components is based on the limited number of inner product operations, performed on the signal with the use of complex exponentials matching pitch characteristics of the separated signals, and not on the discrete spectral representations calculated via DFT. In this paper examples of separation results are presented and each algorithm performance is analyzed. The effectiveness of separation algorithms consists in calculation of feature vectors (FVs) derived from musical sounds after the separation process is performed, and then in feeding them the Neural Network (NN) for automatic musical sound identification. The experimental results are shown and discussed. A comparison of effectiveness of all presented algorithms is also included, and conclusions are derived.