Abstract
Blind separation of musical sounds contained in sound mixtures is a challenging and difficult task. It is due to the fact that in Western music, mixed harmonic sources may be correlated with each other, i.e. their harmonic partials might be overlapping in the frequency domain if the signals remain in harmonic relation. Evaluation of the separation results is also problematic, since analysis of the energy-based error between the original signals used for mixing and the separated ones, in some cases, do not correspond with perceptual evaluation results. In this paper, four separation algorithms, engineered by the Authors, are presented. Then, musical instrument sound identification based on artificial neural networks is performed as a means of evaluating the performance of the separation algorithms. Results are discussed and conclusions are derived.
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Dziubinski, M., Dalka, P. & Kostek, B. Estimation of Musical Sound Separation Algorithm Effectiveness Employing Neural Networks. J Intell Inf Syst 24, 133–157 (2005). https://doi.org/10.1007/s10844-005-0320-x
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DOI: https://doi.org/10.1007/s10844-005-0320-x