Estimation of Source Panning Parameters and Segmentation of Stereophonic Mixtures

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Abstract

In this paper, we propose a method for finding the number of sources and their parameters from stereophonic mixtures. The method is based on clustering of narrowband interaural level and time differences for an unknown number of sources and uses an optimal segmentation on which the clustering is based. The parameter distribution, for both individual seg- ments and across segments that comprise the entire signal, is modelled as a Gaussian mixture. For each segment parame- ters are estimated using a minimum description length algo- rithm for mixtures based on the expectation-maximization al- gorithm. The generalized variance and degree of membership of the Gaussian components across segments is used as a ba- sis for the proposed selection of clusters amongst candidates. Simulations on synthetic and real audio shows promising re- sults for source parameter estimation and number of sources estimated across segments. The optimal segmentation shows an improvement for parameter estimation success rate, com- pared to the uniform segmentation.
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Detaljer

In this paper, we propose a method for finding the number of sources and their parameters from stereophonic mixtures. The method is based on clustering of narrowband interaural level and time differences for an unknown number of sources and uses an optimal segmentation on which the clustering is based. The parameter distribution, for both individual seg- ments and across segments that comprise the entire signal, is modelled as a Gaussian mixture. For each segment parame- ters are estimated using a minimum description length algo- rithm for mixtures based on the expectation-maximization al- gorithm. The generalized variance and degree of membership of the Gaussian components across segments is used as a ba- sis for the proposed selection of clusters amongst candidates. Simulations on synthetic and real audio shows promising re- sults for source parameter estimation and number of sources estimated across segments. The optimal segmentation shows an improvement for parameter estimation success rate, com- pared to the uniform segmentation.
OriginalsprogEngelsk
TitelIEEE International Conference on Acoustics, Speech and Signal Processing
ForlagIEEE
Publikationsdato15 apr. 2018
Sider426-430
ISBN (Trykt)978-1-5386-4659-5
ISBN (Elektronisk)978-1-5386-4658-8
DOI
StatusUdgivet - 15 apr. 2018
PublikationsartForskning
Peer reviewJa
Begivenhed2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada
Varighed: 15 apr. 201820 apr. 2018
https://2018.ieeeicassp.org/

Konference

Konference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
LandCanada
ByCalgary
Periode15/04/201820/04/2018
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