[{"date_created":"2019-07-12T05:29:42Z","department":[{"_id":"54"}],"keyword":["Bayes methods","compensation","error statistics","reverberation","speech recognition","Bayesian feature enhancement","background noise","clean speech feature vectors","compensation","connected digits recognition task","error statistics","memory requirements","noisy reverberant data","posteriori probability density function","recursive formulation","reverberant logarithmic mel power spectral coefficients","robust automatic speech recognition","signal-to-noise ratios","time-variant observation","word error rate reduction","Robust automatic speech recognition","model-based Bayesian feature enhancement","observation model for reverberant and noisy speech","recursive observation model"],"type":"journal_article","citation":{"apa":"Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2013). Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, <i>21</i>(8), 1640–1652. <a href=\"https://doi.org/10.1109/TASL.2013.2258013\">https://doi.org/10.1109/TASL.2013.2258013</a>","mla":"Leutnant, Volker, et al. “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 21, no. 8, 2013, pp. 1640–52, doi:<a href=\"https://doi.org/10.1109/TASL.2013.2258013\">10.1109/TASL.2013.2258013</a>.","ieee":"V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 21, no. 8, pp. 1640–1652, 2013.","chicago":"Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i> 21, no. 8 (2013): 1640–52. <a href=\"https://doi.org/10.1109/TASL.2013.2258013\">https://doi.org/10.1109/TASL.2013.2258013</a>.","ama":"Leutnant V, Krueger A, Haeb-Umbach R. Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>. 2013;21(8):1640-1652. doi:<a href=\"https://doi.org/10.1109/TASL.2013.2258013\">10.1109/TASL.2013.2258013</a>","short":"V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 21 (2013) 1640–1652.","bibtex":"@article{Leutnant_Krueger_Haeb-Umbach_2013, title={Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition}, volume={21}, DOI={<a href=\"https://doi.org/10.1109/TASL.2013.2258013\">10.1109/TASL.2013.2258013</a>}, number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2013}, pages={1640–1652} }"},"publication":"IEEE Transactions on Audio, Speech, and Language Processing","issue":"8","abstract":[{"lang":"eng","text":"In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data."}],"language":[{"iso":"eng"}],"_id":"11862","page":"1640-1652","volume":21,"doi":"10.1109/TASL.2013.2258013","user_id":"44006","author":[{"full_name":"Leutnant, Volker","first_name":"Volker","last_name":"Leutnant"},{"last_name":"Krueger","first_name":"Alexander","full_name":"Krueger, Alexander"},{"last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"title":"Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition","status":"public","year":"2013","intvolume":"        21","date_updated":"2022-01-06T06:51:11Z"},{"oa":"1","citation":{"bibtex":"@inproceedings{Haeb-Umbach_Bevermeier_2007, title={OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain}, volume={3}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">10.1109/ICASSP.2007.366526</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)}, author={Haeb-Umbach, Reinhold and Bevermeier, Maik}, year={2007}, pages={III-277-III–280} }","ama":"Haeb-Umbach R, Bevermeier M. OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>. Vol 3. ; 2007:III-277-III-280. doi:<a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">10.1109/ICASSP.2007.366526</a>","mla":"Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, vol. 3, 2007, pp. III-277-III–280, doi:<a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">10.1109/ICASSP.2007.366526</a>.","chicago":"Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, 3:III-277-III–280, 2007. <a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">https://doi.org/10.1109/ICASSP.2007.366526</a>.","short":"R. Haeb-Umbach, M. Bevermeier, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), 2007, pp. III-277-III–280.","ieee":"R. Haeb-Umbach and M. Bevermeier, “OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, 2007, vol. 3, pp. III-277-III–280.","apa":"Haeb-Umbach, R., &#38; Bevermeier, M. (2007). OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i> (Vol. 3, pp. III-277-III–280). <a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">https://doi.org/10.1109/ICASSP.2007.366526</a>"},"user_id":"44006","volume":3,"page":"III-277-III-280","_id":"11785","status":"public","keyword":["bit error rate","block-oriented OFDM transmission","channel estimation","channel impulse response estimation","combining estimators","error statistics","frequency domain estimation","Gaussian mean vectors","Gaussian processes","Kalman filter","Kalman filters","MAP estimator","maximum likelihood estimation","OFDM channel estimation","OFDM modulation","time domain estimation","time-frequency analysis","Wiener filter","Wiener filters"],"type":"conference","department":[{"_id":"54"}],"date_created":"2019-07-12T05:28:13Z","abstract":[{"lang":"eng","text":"In this paper we present a novel channel impulse response estimation technique for block-oriented OFDM transmission based on combining estimators: the estimates provided by a Kalman filter operating in the time domain and a Wiener filter in the frequency domain are optimally combined by taking into account their estimated error covariances. The resulting estimator turns out to be identical to the MAP estimator of correlated jointly Gaussian mean vectors. Different variants of the proposed scheme are experimentally investigated in an EEEE 802.11a-like system setup. They compare favourably with known approaches from the literature resulting in reduced mean square estimation error and bit error rate. Further, robustness and complexity issues are discussed"}],"publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)","doi":"10.1109/ICASSP.2007.366526","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2007/HaBe07.pdf","open_access":"1"}],"language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:51:08Z","intvolume":"         3","year":"2007","title":"OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain","author":[{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"},{"last_name":"Bevermeier","first_name":"Maik","full_name":"Bevermeier, Maik"}]},{"_id":"11870","page":"762-766","volume":23,"user_id":"44006","status":"public","oa":"1","citation":{"mla":"Loog, M., et al. “Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 23, no. 7, 2001, pp. 762–66, doi:<a href=\"https://doi.org/10.1109/34.935849\">10.1109/34.935849</a>.","ama":"Loog M, Duin RPW, Haeb-Umbach R. Multiclass linear dimension reduction by weighted pairwise Fisher criteria. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2001;23(7):762-766. doi:<a href=\"https://doi.org/10.1109/34.935849\">10.1109/34.935849</a>","bibtex":"@article{Loog_Duin_Haeb-Umbach_2001, title={Multiclass linear dimension reduction by weighted pairwise Fisher criteria}, volume={23}, DOI={<a href=\"https://doi.org/10.1109/34.935849\">10.1109/34.935849</a>}, number={7}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Loog, M. and Duin, R.P.W. and Haeb-Umbach, Reinhold}, year={2001}, pages={762–766} }","apa":"Loog, M., Duin, R. P. W., &#38; Haeb-Umbach, R. (2001). Multiclass linear dimension reduction by weighted pairwise Fisher criteria. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, <i>23</i>(7), 762–766. <a href=\"https://doi.org/10.1109/34.935849\">https://doi.org/10.1109/34.935849</a>","ieee":"M. Loog, R. P. W. Duin, and R. Haeb-Umbach, “Multiclass linear dimension reduction by weighted pairwise Fisher criteria,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 23, no. 7, pp. 762–766, 2001.","short":"M. Loog, R.P.W. Duin, R. Haeb-Umbach, IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (2001) 762–766.","chicago":"Loog, M., R.P.W. Duin, and Reinhold Haeb-Umbach. “Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i> 23, no. 7 (2001): 762–66. <a href=\"https://doi.org/10.1109/34.935849\">https://doi.org/10.1109/34.935849</a>."},"language":[{"iso":"eng"}],"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2001/LoDuHa01.pdf","open_access":"1"}],"doi":"10.1109/34.935849","author":[{"first_name":"M.","last_name":"Loog","full_name":"Loog, M."},{"full_name":"Duin, R.P.W.","first_name":"R.P.W.","last_name":"Duin"},{"full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold","id":"242"}],"year":"2001","title":"Multiclass linear dimension reduction by weighted pairwise Fisher criteria","intvolume":"        23","date_updated":"2022-01-06T06:51:11Z","date_created":"2019-07-12T05:29:51Z","department":[{"_id":"54"}],"type":"journal_article","keyword":["approximate pairwise accuracy","Bayes error","Bayes methods","error statistics","Euclidean distance","Fisher criterion","linear dimension reduction","linear discriminant analysis","pattern classification","statistical analysis","statistical pattern classification","weighting function"],"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issue":"7","abstract":[{"lang":"eng","text":"We derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidean distance of the respective class means. We generalize upon LDA by introducing a different weighting function"}]}]