Comparison of Effectiveness of Musical Sound Separation Algorithms Employing Neural Networks

Dalka P, Dziubinski M, Kostek B. Comparison of Effectiveness of Musical Sound Separation Algorithms Employing Neural Networks. In: AES Convention 117. Audio Engineering Society; 2004. Paper 6310. Available from: https://aes.org/publications/elibrary-page/?id=12967

@inproceedings{Dalka2004_12967,
  author = {Dalka, Piotr and Dziubinski, Marek and Kostek, Bozena},
  title = {{Comparison of Effectiveness of Musical Sound Separation Algorithms Employing Neural Networks}},
  booktitle = {AES Convention 117},
  note = {Paper 6310},
  year = {2004},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=12967}
}

TY  - CPAPER
TI  - Comparison of Effectiveness of Musical Sound Separation Algorithms Employing Neural Networks
AU  - Dalka, Piotr
AU  - Dziubinski, Marek
AU  - Kostek, Bozena
T2  - AES Convention 117
M1  - Paper 6310
PY  - 2004
DA  - 2004/10/06
UR  - https://aes.org/publications/elibrary-page/?id=12967
PB  - Audio Engineering Society
LA  - en
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.
ER  -