DocumentCode :
61926
Title :
Compositional Models for Audio Processing: Uncovering the structure of sound mixtures
Author :
Virtanen, Tuomas ; Gemmeke, Jort Florent ; Raj, Bhiksha ; Smaragdis, Paris
Author_Institution :
Dept. of Signal Process., Tampere Univ. of Technol., Tampere, Finland
Volume :
32
Issue :
2
fYear :
2015
fDate :
Mar-15
Firstpage :
125
Lastpage :
144
Abstract :
Many classes of data are composed as constructive combinations of parts. By constructive combination, we mean additive combination that does not result in subtraction or diminishment of any of the parts. We will refer to such data as compositional data. Typical examples include population or counts data, where the total count of a population is obtained as the sum of counts of subpopulations. To characterize such data, various mathematical models have been developed in the literature. These models, in conformance with the nature of the data, represent them as nonnegative linear combinations of parts, which themselves are also nonnegative to ensure that such a combination does not result in subtraction or diminishment. We will refer to such models as compositional models.
Keywords :
audio signal processing; data handling; audio processing; compositional data; compositional models; counts data; mathematical models; nonnegative linear part combinations; population data; sound mixture structure; sum-of-subpopulation counts; Acoustic signal processing; Atomic clocks; Matrix decomposition; Principal component analysis; Signal resolution; Spectrogram; Time-frequency analysis;
fLanguage :
English
Journal_Title :
Signal Processing Magazine, IEEE
Publisher :
ieee
ISSN :
1053-5888
Type :
jour
DOI :
10.1109/MSP.2013.2288990
Filename :
7038275
Link To Document :
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