TY - GEN
T1 - Collaborative audio enhancement using probabilistic latent component sharing
AU - Kim, Minje
AU - Smaragdis, Paris
PY - 2013/10/18
Y1 - 2013/10/18
N2 - This paper presents a collaborative audio enhancement system that aims to recover common audio sources from multiple recordings of a given audio scene. We do so in the context where each recording is uniquely corrupted. To this end, we propose a method of simultaneous probabilistic latent component analyses on synchronized inputs. In the proposed model, some of the parameters are fixed to be same during and after the learning process to capture common audio content while the rest models unwanted recording-specific interferences and artifacts. Our model also allows for prior knowledge about the parameters of the model, e.g. representative spectra of the components, to be incorporated in the factorization. A post processing scheme that consolidates the extracted sources from the set of inputs is also proposed to handle the possible loss of certain frequency regions. Experiments on commercial music signals with various artifacts show the merit of the proposed method.
AB - This paper presents a collaborative audio enhancement system that aims to recover common audio sources from multiple recordings of a given audio scene. We do so in the context where each recording is uniquely corrupted. To this end, we propose a method of simultaneous probabilistic latent component analyses on synchronized inputs. In the proposed model, some of the parameters are fixed to be same during and after the learning process to capture common audio content while the rest models unwanted recording-specific interferences and artifacts. Our model also allows for prior knowledge about the parameters of the model, e.g. representative spectra of the components, to be incorporated in the factorization. A post processing scheme that consolidates the extracted sources from the set of inputs is also proposed to handle the possible loss of certain frequency regions. Experiments on commercial music signals with various artifacts show the merit of the proposed method.
KW - Convolutive Common Nonnegative Matrix Factorization
KW - Crowdsourcing
KW - Nonnegative Matrix Partial Co-Factorization
KW - Probabilistic Latent Component Analysis
UR - http://www.scopus.com/inward/record.url?scp=84890544402&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890544402&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6637778
DO - 10.1109/ICASSP.2013.6637778
M3 - Conference contribution
AN - SCOPUS:84890544402
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 896
EP - 900
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -