[{"year":"2024","page":"1-1","citation":{"ama":"Rodriguez-Fernandez AE, Schäpermeier L, Hernández C, Kerschke P, Trautmann H, Schütze O. Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization. <i>IEEE Transactions on Evolutionary Computation</i>. Published online 2024:1-1. doi:<a href=\"https://doi.org/10.1109/TEVC.2024.3458855\">10.1109/TEVC.2024.3458855</a>","chicago":"Rodriguez-Fernandez, Angel E., Lennart Schäpermeier, Carlos Hernández, Pascal Kerschke, Heike Trautmann, and Oliver Schütze. “Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization.” <i>IEEE Transactions on Evolutionary Computation</i>, 2024, 1–1. <a href=\"https://doi.org/10.1109/TEVC.2024.3458855\">https://doi.org/10.1109/TEVC.2024.3458855</a>.","ieee":"A. E. Rodriguez-Fernandez, L. Schäpermeier, C. Hernández, P. Kerschke, H. Trautmann, and O. Schütze, “Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization,” <i>IEEE Transactions on Evolutionary Computation</i>, pp. 1–1, 2024, doi: <a href=\"https://doi.org/10.1109/TEVC.2024.3458855\">10.1109/TEVC.2024.3458855</a>.","short":"A.E. Rodriguez-Fernandez, L. Schäpermeier, C. Hernández, P. Kerschke, H. Trautmann, O. Schütze, IEEE Transactions on Evolutionary Computation (2024) 1–1.","bibtex":"@article{Rodriguez-Fernandez_Schäpermeier_Hernández_Kerschke_Trautmann_Schütze_2024, title={Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}, DOI={<a href=\"https://doi.org/10.1109/TEVC.2024.3458855\">10.1109/TEVC.2024.3458855</a>}, journal={IEEE Transactions on Evolutionary Computation}, author={Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Hernández, Carlos and Kerschke, Pascal and Trautmann, Heike and Schütze, Oliver}, year={2024}, pages={1–1} }","mla":"Rodriguez-Fernandez, Angel E., et al. “Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization.” <i>IEEE Transactions on Evolutionary Computation</i>, 2024, pp. 1–1, doi:<a href=\"https://doi.org/10.1109/TEVC.2024.3458855\">10.1109/TEVC.2024.3458855</a>.","apa":"Rodriguez-Fernandez, A. E., Schäpermeier, L., Hernández, C., Kerschke, P., Trautmann, H., &#38; Schütze, O. (2024). Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization. <i>IEEE Transactions on Evolutionary Computation</i>, 1–1. <a href=\"https://doi.org/10.1109/TEVC.2024.3458855\">https://doi.org/10.1109/TEVC.2024.3458855</a>"},"date_updated":"2024-09-24T08:01:47Z","date_created":"2024-09-24T08:01:14Z","author":[{"first_name":"Angel E.","full_name":"Rodriguez-Fernandez, Angel E.","last_name":"Rodriguez-Fernandez"},{"first_name":"Lennart","last_name":"Schäpermeier","full_name":"Schäpermeier, Lennart"},{"first_name":"Carlos","full_name":"Hernández, Carlos","last_name":"Hernández"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"first_name":"Oliver","last_name":"Schütze","full_name":"Schütze, Oliver"}],"title":"Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization","doi":"10.1109/TEVC.2024.3458855","publication":"IEEE Transactions on Evolutionary Computation","type":"journal_article","status":"public","_id":"56221","user_id":"15504","keyword":["Optimization","Evolutionary computation","Approximation algorithms","Benchmark testing","Vectors","Surveys","Pareto optimization","multi-objective optimization","evolutionary computation","multimodal optimization","local solutions"],"language":[{"iso":"eng"}]},{"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14_Poster.pdf","description":"Poster","relation":"supplementary_material"}]},"page":"213-217","citation":{"ieee":"L. Drude, A. Chinaev, D. H. Tran Vu, and R. Haeb-Umbach, “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models,” in <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i>, 2014, pp. 213–217.","chicago":"Drude, Lukas, Aleksej Chinaev, Dang Hai Tran Vu, and Reinhold Haeb-Umbach. “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.” In <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i>, 213–17, 2014.","ama":"Drude L, Chinaev A, Tran Vu DH, Haeb-Umbach R. Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models. In: <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i>. ; 2014:213-217.","short":"L. Drude, A. Chinaev, D.H. Tran Vu, R. Haeb-Umbach, in: 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014), 2014, pp. 213–217.","bibtex":"@inproceedings{Drude_Chinaev_Tran Vu_Haeb-Umbach_2014, title={Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models}, booktitle={14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)}, author={Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2014}, pages={213–217} }","mla":"Drude, Lukas, et al. “Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models.” <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i>, 2014, pp. 213–17.","apa":"Drude, L., Chinaev, A., Tran Vu, D. H., &#38; Haeb-Umbach, R. (2014). Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models. In <i>14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)</i> (pp. 213–217)."},"year":"2014","date_created":"2019-07-12T05:27:35Z","author":[{"full_name":"Drude, Lukas","id":"11213","last_name":"Drude","first_name":"Lukas"},{"full_name":"Chinaev, Aleksej","last_name":"Chinaev","first_name":"Aleksej"},{"full_name":"Tran Vu, Dang Hai","last_name":"Tran Vu","first_name":"Dang Hai"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"date_updated":"2022-01-06T06:51:08Z","oa":"1","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2014/DrChTrHaeb14.pdf"}],"title":"Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models","publication":"14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)","type":"conference","status":"public","abstract":[{"lang":"eng","text":"This contribution describes a step-wise source counting algorithm to determine the number of speakers in an offline scenario. Each speaker is identified by a variational expectation maximization (VEM) algorithm for complex Watson mixture models and therefore directly yields beamforming vectors for a subsequent speech separation process. An observation selection criterion is proposed which improves the robustness of the source counting in noise. The algorithm is compared to an alternative VEM approach with Gaussian mixture models based on directions of arrival and shown to deliver improved source counting accuracy. The article concludes by extending the offline algorithm towards a low-latency online estimation of the number of active sources from the streaming input data."}],"department":[{"_id":"54"}],"user_id":"44006","_id":"11753","language":[{"iso":"eng"}],"keyword":["Accuracy","Acoustics","Estimation","Mathematical model","Soruce separation","Speech","Vectors","Bayes methods","Blind source separation","Directional statistics","Number of speakers","Speaker diarization"]},{"keyword":["computational complexity","reverberation","speech recognition","automatic speech recognition","background noise","clean speech","computational complexity","energy compensation","logarithmic mel power spectral domain","mel frequency cepstral coefficients","microphone input signals","model-based feature compensation schemes","noisy reverberant speech automatic recognition","noisy reverberant speech features","reverberation","Atmospheric modeling","Computational modeling","Noise","Noise measurement","Reverberation","Speech","Vectors","Model-based feature compensation","observation model for reverberant and noisy speech","recursive observation model","robust automatic speech recognition"],"language":[{"iso":"eng"}],"_id":"11861","department":[{"_id":"54"}],"user_id":"44006","abstract":[{"lang":"eng","text":"In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction between clean speech, noise, and noisy reverberant speech features is essential for any ASR system to be robust against noise and reverberation present in distant microphone input signals. The findings are gathered in a probabilistic formulation of an observation model which may be used in model-based feature compensation schemes. The proposed observation model extends previous models in three major directions: First, the contribution of additive background noise to the observation error is explicitly taken into account. Second, an energy compensation constant is introduced which ensures an unbiased estimate of the reverberant speech features, and, third, a recursive variant of the observation model is developed resulting in reduced computational complexity when used in model-based feature compensation. The experimental section is used to evaluate the accuracy of the model and to describe how its parameters can be determined from test data."}],"status":"public","publication":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","type":"journal_article","title":"A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech","doi":"10.1109/TASLP.2013.2285480","date_updated":"2022-01-06T06:51:11Z","volume":22,"date_created":"2019-07-12T05:29:41Z","author":[{"last_name":"Leutnant","full_name":"Leutnant, Volker","first_name":"Volker"},{"last_name":"Krueger","full_name":"Krueger, Alexander","first_name":"Alexander"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"year":"2014","page":"95-109","intvolume":"        22","citation":{"chicago":"Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.” <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i> 22, no. 1 (2014): 95–109. <a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">https://doi.org/10.1109/TASLP.2013.2285480</a>.","ieee":"V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech,” <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>, vol. 22, no. 1, pp. 95–109, 2014.","ama":"Leutnant V, Krueger A, Haeb-Umbach R. A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech. <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>. 2014;22(1):95-109. doi:<a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">10.1109/TASLP.2013.2285480</a>","apa":"Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2014). A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech. <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>, <i>22</i>(1), 95–109. <a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">https://doi.org/10.1109/TASLP.2013.2285480</a>","short":"V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE/ACM Transactions on Audio, Speech, and Language Processing 22 (2014) 95–109.","bibtex":"@article{Leutnant_Krueger_Haeb-Umbach_2014, title={A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech}, volume={22}, DOI={<a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">10.1109/TASLP.2013.2285480</a>}, number={1}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2014}, pages={95–109} }","mla":"Leutnant, Volker, et al. “A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.” <i>IEEE/ACM Transactions on Audio, Speech, and Language Processing</i>, vol. 22, no. 1, 2014, pp. 95–109, doi:<a href=\"https://doi.org/10.1109/TASLP.2013.2285480\">10.1109/TASLP.2013.2285480</a>."},"publication_identifier":{"issn":["2329-9290"]},"issue":"1"},{"volume":21,"date_created":"2020-08-06T15:22:05Z","author":[{"last_name":"Cohen","full_name":"Cohen, R.","first_name":"R."},{"full_name":"Nudelman, I.","last_name":"Nudelman","first_name":"I."},{"first_name":"Gleb","last_name":"Polevoy","full_name":"Polevoy, Gleb","id":"83983"}],"date_updated":"2022-01-06T06:53:16Z","doi":"10.1109/TNET.2012.2227792","title":"On the Admission of Dependent Flows in Powerful Sensor Networks","issue":"5","publication_identifier":{"issn":["1063-6692"]},"page":"1461-1471","intvolume":"        21","citation":{"ama":"Cohen R, Nudelman I, Polevoy G. On the Admission of Dependent Flows in Powerful Sensor Networks. <i>Networking, IEEE/ACM Transactions on</i>. 2013;21(5):1461-1471. doi:<a href=\"https://doi.org/10.1109/TNET.2012.2227792\">10.1109/TNET.2012.2227792</a>","chicago":"Cohen, R., I. Nudelman, and Gleb Polevoy. “On the Admission of Dependent Flows in Powerful Sensor Networks.” <i>Networking, IEEE/ACM Transactions On</i> 21, no. 5 (2013): 1461–71. <a href=\"https://doi.org/10.1109/TNET.2012.2227792\">https://doi.org/10.1109/TNET.2012.2227792</a>.","ieee":"R. Cohen, I. Nudelman, and G. Polevoy, “On the Admission of Dependent Flows in Powerful Sensor Networks,” <i>Networking, IEEE/ACM Transactions on</i>, vol. 21, no. 5, pp. 1461–1471, 2013.","mla":"Cohen, R., et al. “On the Admission of Dependent Flows in Powerful Sensor Networks.” <i>Networking, IEEE/ACM Transactions On</i>, vol. 21, no. 5, 2013, pp. 1461–71, doi:<a href=\"https://doi.org/10.1109/TNET.2012.2227792\">10.1109/TNET.2012.2227792</a>.","bibtex":"@article{Cohen_Nudelman_Polevoy_2013, title={On the Admission of Dependent Flows in Powerful Sensor Networks}, volume={21}, DOI={<a href=\"https://doi.org/10.1109/TNET.2012.2227792\">10.1109/TNET.2012.2227792</a>}, number={5}, journal={Networking, IEEE/ACM Transactions on}, author={Cohen, R. and Nudelman, I. and Polevoy, Gleb}, year={2013}, pages={1461–1471} }","short":"R. Cohen, I. Nudelman, G. Polevoy, Networking, IEEE/ACM Transactions On 21 (2013) 1461–1471.","apa":"Cohen, R., Nudelman, I., &#38; Polevoy, G. (2013). On the Admission of Dependent Flows in Powerful Sensor Networks. <i>Networking, IEEE/ACM Transactions On</i>, <i>21</i>(5), 1461–1471. <a href=\"https://doi.org/10.1109/TNET.2012.2227792\">https://doi.org/10.1109/TNET.2012.2227792</a>"},"year":"2013","department":[{"_id":"63"},{"_id":"541"}],"user_id":"83983","_id":"17663","extern":"1","language":[{"iso":"eng"}],"keyword":["Approximation algorithms","Approximation methods","Bandwidth","Logic gates","Radar","Vectors","Wireless sensor networks","Dependent flow scheduling","sensor networks"],"publication":"Networking, IEEE/ACM Transactions on","type":"journal_article","status":"public","abstract":[{"lang":"eng","text":"In this paper, we define and study a new problem, referred to as the Dependent Unsplittable Flow Problem (D-UFP). We present and discuss this problem in the context of large-scale powerful (radar/camera) sensor networks, but we believe it has important applications on the admission of large flows in other networks as well. In order to optimize the selection of flows transmitted to the gateway, D-UFP takes into account possible dependencies between flows. We show that D-UFP is more difficult than NP-hard problems for which no good approximation is known. Then, we address two special cases of this problem: the case where all the sensors have a shared channel and the case where the sensors form a mesh and route to the gateway over a spanning tree."}]},{"title":"Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition","doi":"10.1109/TASL.2013.2258013","date_updated":"2022-01-06T06:51:11Z","date_created":"2019-07-12T05:29:42Z","author":[{"first_name":"Volker","full_name":"Leutnant, Volker","last_name":"Leutnant"},{"first_name":"Alexander","last_name":"Krueger","full_name":"Krueger, Alexander"},{"full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"volume":21,"year":"2013","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>","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} }","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>.","short":"V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 21 (2013) 1640–1652.","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>.","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.","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>"},"page":"1640-1652","intvolume":"        21","issue":"8","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"],"language":[{"iso":"eng"}],"_id":"11862","user_id":"44006","department":[{"_id":"54"}],"abstract":[{"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.","lang":"eng"}],"status":"public","type":"journal_article","publication":"IEEE Transactions on Audio, Speech, and Language Processing"},{"language":[{"iso":"eng"}],"keyword":["correlation methods","estimation theory","hidden Markov models","iterative methods","probability","spectral analysis","speech processing","2D HMM","SPP estimates","iterative algorithm","posterior probability estimation","spectral correlation","speech presence probability estimation","state-of-the-art SPP estimation algorithm","temporal correlation","turbo principle","two-dimensional hidden Markov model","Correlation","Decoding","Estimation","Iterative decoding","Noise","Speech","Vectors"],"user_id":"44006","department":[{"_id":"54"}],"_id":"11917","status":"public","abstract":[{"text":"In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm.","lang":"eng"}],"type":"conference","publication":"38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)","doi":"10.1109/ICASSP.2013.6637771","title":"Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation","date_created":"2019-07-12T05:30:45Z","author":[{"full_name":"Vu, Dang Hai Tran","last_name":"Vu","first_name":"Dang Hai Tran"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"date_updated":"2022-01-06T06:51:12Z","citation":{"bibtex":"@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>}, booktitle={38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013}, pages={863–867} }","short":"D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.","mla":"Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 863–67, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>.","apa":"Vu, D. H. T., &#38; Haeb-Umbach, R. (2013). Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i> (pp. 863–867). <a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">https://doi.org/10.1109/ICASSP.2013.6637771</a>","ama":"Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In: <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:863-867. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>","ieee":"D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation,” in <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 863–867.","chicago":"Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 863–67, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">https://doi.org/10.1109/ICASSP.2013.6637771</a>."},"page":"863-867","year":"2013","publication_identifier":{"issn":["1520-6149"]}},{"type":"journal_article","status":"public","department":[{"_id":"54"}],"user_id":"44006","_id":"11846","page":"1692-1707","intvolume":"        18","citation":{"chicago":"Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement for Reverberant Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i> 18, no. 7 (2010): 1692–1707. <a href=\"https://doi.org/10.1109/TASL.2010.2049684\">https://doi.org/10.1109/TASL.2010.2049684</a>.","ieee":"A. Krueger and R. Haeb-Umbach, “Model-Based Feature Enhancement for Reverberant Speech Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 18, no. 7, pp. 1692–1707, 2010.","ama":"Krueger A, Haeb-Umbach R. Model-Based Feature Enhancement for Reverberant Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>. 2010;18(7):1692-1707. doi:<a href=\"https://doi.org/10.1109/TASL.2010.2049684\">10.1109/TASL.2010.2049684</a>","apa":"Krueger, A., &#38; Haeb-Umbach, R. (2010). Model-Based Feature Enhancement for Reverberant Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, <i>18</i>(7), 1692–1707. <a href=\"https://doi.org/10.1109/TASL.2010.2049684\">https://doi.org/10.1109/TASL.2010.2049684</a>","short":"A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 18 (2010) 1692–1707.","bibtex":"@article{Krueger_Haeb-Umbach_2010, title={Model-Based Feature Enhancement for Reverberant Speech Recognition}, volume={18}, DOI={<a href=\"https://doi.org/10.1109/TASL.2010.2049684\">10.1109/TASL.2010.2049684</a>}, number={7}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2010}, pages={1692–1707} }","mla":"Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement for Reverberant Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 18, no. 7, 2010, pp. 1692–707, doi:<a href=\"https://doi.org/10.1109/TASL.2010.2049684\">10.1109/TASL.2010.2049684</a>."},"volume":18,"author":[{"last_name":"Krueger","full_name":"Krueger, Alexander","first_name":"Alexander"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"oa":"1","date_updated":"2022-01-06T06:51:11Z","doi":"10.1109/TASL.2010.2049684","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2010/KrHa10.pdf","open_access":"1"}],"publication":"IEEE Transactions on Audio, Speech, and Language Processing","abstract":[{"text":"In this paper, we present a new technique for automatic speech recognition (ASR) in reverberant environments. Our approach is aimed at the enhancement of the logarithmic Mel power spectrum, which is computed at an intermediate stage to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean square error estimate of the clean LMPSCs is computed by carrying out Bayesian inference. We employ switching linear dynamical models as an a priori model for the dynamics of the clean LMPSCs. Further, we derive a stochastic observation model which relates the clean to the reverberant LMPSCs through a simplified model of the room impulse response (RIR). This model requires only two parameters, namely RIR energy and reverberation time, which can be estimated from the captured microphone signal. The performance of the proposed enhancement technique is studied on the AURORA5 database and compared to that of constrained maximum-likelihood linear regression (CMLLR). It is shown by experimental results that our approach significantly outperforms CMLLR and that up to 80\\% of the errors caused by the reverberation are recovered. In addition to the fact that the approach is compatible with the standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of moderate computational complexity and suitable for real time applications.","lang":"eng"}],"language":[{"iso":"eng"}],"keyword":["ASR","AURORA5 database","automatic speech recognition","Bayesian inference","belief networks","CMLLR","computational complexity","constrained maximum likelihood linear regression","least mean squares methods","LMPSC computation","logarithmic Mel power spectrum","maximum likelihood estimation","Mel frequency cepstral coefficients","MFCC feature vectors","microphone signal","minimum mean square error estimation","model-based feature enhancement","regression analysis","reverberant speech recognition","reverberation","RIR energy","room impulse response","speech recognition","stochastic observation model","stochastic processes"],"issue":"7","year":"2010","date_created":"2019-07-12T05:29:23Z","title":"Model-Based Feature Enhancement for Reverberant Speech Recognition"},{"date_updated":"2024-08-08T12:18:04Z","date_created":"2018-06-05T08:07:24Z","author":[{"last_name":"Blömer","full_name":"Blömer, Johannes","id":"23","first_name":"Johannes"},{"first_name":"Stefanie","full_name":" Naewe, Stefanie","id":"1971","last_name":" Naewe"}],"volume":410,"title":"Sampling methods for shortest vectors, closest vectors and successive minima","doi":"10.1016/j.tcs.2008.12.045","publication_status":"published","publication_identifier":{"issn":["0304-3975"]},"issue":"18","year":"2009","citation":{"bibtex":"@article{Blömer_ Naewe_2009, title={Sampling methods for shortest vectors, closest vectors and successive minima}, volume={410}, DOI={<a href=\"https://doi.org/10.1016/j.tcs.2008.12.045\">10.1016/j.tcs.2008.12.045</a>}, number={18}, journal={Theoretical Computer Science}, author={Blömer, Johannes and  Naewe, Stefanie}, year={2009}, pages={1648–1665} }","mla":"Blömer, Johannes, and Stefanie  Naewe. “Sampling Methods for Shortest Vectors, Closest Vectors and Successive Minima.” <i>Theoretical Computer Science</i>, vol. 410, no. 18, 2009, pp. 1648–65, doi:<a href=\"https://doi.org/10.1016/j.tcs.2008.12.045\">10.1016/j.tcs.2008.12.045</a>.","short":"J. Blömer, S.  Naewe, Theoretical Computer Science 410 (2009) 1648–1665.","apa":"Blömer, J., &#38;  Naewe, S. (2009). Sampling methods for shortest vectors, closest vectors and successive minima. <i>Theoretical Computer Science</i>, <i>410</i>(18), 1648–1665. <a href=\"https://doi.org/10.1016/j.tcs.2008.12.045\">https://doi.org/10.1016/j.tcs.2008.12.045</a>","chicago":"Blömer, Johannes, and Stefanie  Naewe. “Sampling Methods for Shortest Vectors, Closest Vectors and Successive Minima.” <i>Theoretical Computer Science</i> 410, no. 18 (2009): 1648–65. <a href=\"https://doi.org/10.1016/j.tcs.2008.12.045\">https://doi.org/10.1016/j.tcs.2008.12.045</a>.","ieee":"J. Blömer and S.  Naewe, “Sampling methods for shortest vectors, closest vectors and successive minima,” <i>Theoretical Computer Science</i>, vol. 410, no. 18, pp. 1648–1665, 2009, doi: <a href=\"https://doi.org/10.1016/j.tcs.2008.12.045\">10.1016/j.tcs.2008.12.045</a>.","ama":"Blömer J,  Naewe S. Sampling methods for shortest vectors, closest vectors and successive minima. <i>Theoretical Computer Science</i>. 2009;410(18):1648-1665. doi:<a href=\"https://doi.org/10.1016/j.tcs.2008.12.045\">10.1016/j.tcs.2008.12.045</a>"},"page":"1648 - 1665","intvolume":"       410","_id":"2999","user_id":"49063","department":[{"_id":"64"}],"keyword":["Geometry of numbers","Lattices","Shortest vectors"],"language":[{"iso":"eng"}],"type":"journal_article","publication":"Theoretical Computer Science","status":"public"},{"oa":"1","date_updated":"2022-01-06T06:51:08Z","volume":3,"author":[{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"},{"full_name":"Bevermeier, Maik","last_name":"Bevermeier","first_name":"Maik"}],"date_created":"2019-07-12T05:28:13Z","title":"OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain","doi":"10.1109/ICASSP.2007.366526","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2007/HaBe07.pdf"}],"year":"2007","intvolume":"         3","page":"III-277-III-280","citation":{"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>.","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} }","short":"R. Haeb-Umbach, M. Bevermeier, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), 2007, 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>","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>","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>.","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."},"_id":"11785","department":[{"_id":"54"}],"user_id":"44006","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"],"language":[{"iso":"eng"}],"publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)","type":"conference","abstract":[{"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","lang":"eng"}],"status":"public"}]