%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{lathoud05c,
                      author = {Lathoud, Guillaume and Magimai-Doss, Mathew and Mesot, Bertrand},
                    projects = {Idiap},
                       month = {9},
                       title = {{A} {S}pectrogram {M}odel for Enhanced {S}ource {L}ocalization and Noise-{R}obust {ASR}},
                   booktitle = {Proceedings of {INTERSPEECH} 2005},
                        year = {2005},
                     address = {Lisbon, Portugal},
                        note = {IDIAP-RR 05-13},
                    crossref = {lathoud-rr-05-13},
                    abstract = {This paper proposes a simple, computationally efficient 2-mixture model approach to discrimination between speech and background noise. It is directly derived from observations on real data, and can be used in a fully unsupervised manner, with the EM algorithm. A first application to sector-based, joint audio source localization and detection, using multiple microphones, confirms that the model can provide major enhancement. A second application to the single channel speech recognition task in a noisy environment yields major improvement on stationary noise and promising results on non-stationary noise.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2005/lathoud05c.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/papers/2005/lathoud05c.ps.gz},
ipdinar={2005},
ipdmembership={speech},
}



crossreferenced publications: 
@TECHREPORT{lathoud-rr-05-13,
                      author = {Lathoud, Guillaume and Magimai-Doss, Mathew and Mesot, Bertrand},
                    projects = {Idiap},
                       title = {A {F}requency-{D}omain {S}ilence Noise {M}odel},
                        type = {Idiap-RR},
                      number = {Idiap-RR-13-2005},
                        year = {2005},
                 institution = {IDIAP},
                     address = {Martigny, Switzerland},
                        note = {To appear in ``Proceedings of INTERSPEECH 2005''},
                    abstract = {This paper proposes a simple, computationally efficient 2-mixture model approach to discrimination between speech and background noise. It is directly derived from observations on real data, and can be used in a fully unsupervised manner, with the EM algorithm. A first application to sector-based, joint audio source localization and detection, using multiple microphones, confirms that the model can provide major enhancement. A second application to the single channel speech recognition task in a noisy environment yields major improvement on stationary noise and promising results on non-stationary noise.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2005/rr-05-13.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2005/rr-05-13.ps.gz},
ipdinar={2005},
ipdmembership={speech},
language={English},
}