DocumentCode :
2171796
Title :
Optimal cost function and magnitude power for NMF-based speech separation and music interpolation
Author :
King, Brian ; Févotte, Cédric ; Smaragdis, Paris
Author_Institution :
Dept. of Electr. Eng., Univ. of Washington, Seattle, WA, USA
fYear :
2012
fDate :
23-26 Sept. 2012
Firstpage :
1
Lastpage :
6
Abstract :
There has been a significant amount of research in new algorithms and applications for nonnegative matrix factorization, but relatively little has been published on practical considerations for real-world applications, such as choosing optimal parameters for a particular application. In this paper, we will look at two applications, single-channel source separation of speech and interpolating missing music data. We will present the optimal parameters found for the experiments as well as discuss how parameters affect performance.
Keywords :
interpolation; matrix decomposition; music; speech processing; NMF-based speech separation; magnitude power; missing music data; music interpolation; nonnegative matrix factorization; optimal cost function; single-channel source separation; Cost function; Interpolation; Source separation; Speech; Training data; Tunneling magnetoresistance; Vectors; Nonnegative matrix factorization; source separation; spectrogram interpolation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on
Conference_Location :
Santander
ISSN :
1551-2541
Print_ISBN :
978-1-4673-1024-6
Electronic_ISBN :
1551-2541
Type :
conf
DOI :
10.1109/MLSP.2012.6349726
Filename :
6349726
Link To Document :
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