TY - JOUR
T1 - Compositional models for audio processing
T2 - Uncovering the structure of sound mixtures
AU - Virtanen, Tuomas
AU - Gemmeke, Jort Florent
AU - Raj, Bhiksha
AU - Smaragdis, Paris
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/3/1
Y1 - 2015/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85032751297&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032751297&partnerID=8YFLogxK
U2 - 10.1109/MSP.2013.2288990
DO - 10.1109/MSP.2013.2288990
M3 - Article
AN - SCOPUS:85032751297
SN - 1053-5888
VL - 32
SP - 125
EP - 144
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
IS - 2
M1 - 7038275
ER -