A maximum likelihood approach to single-channel source separation
The Journal of Machine Learning Research
Separation of harmonic sound sources using sinusoidal modeling
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Multipitch estimation and sound separation by the spectral smoothness principle
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Note separation of polyphonic music by energy split
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
Unsupervised analysis of polyphonic music by sparse coding
IEEE Transactions on Neural Networks
A comprehensive approach for speech related multimedia applications
WSEAS Transactions on Signal Processing
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This article presents a new approach to sound source separation. The introduced algorithm is based on spectral modeling of real instruments. The separation of independent sources is carried out by dividing the energy of the mixture signal based on these instrument models. This way it is possible to regain some of the information that was lost when the independent sources were mixed together into a single signal. The paper presents the theory behind the proposed separation system, then focuses on the instrument model that is the basic element of the approach. Measurement results are given for polyphony levels from 2 to 10 demonstrating the separation quality, with special regard to the effect of prints on the result.