[{"author":[{"id":"46447","full_name":"Sievers, Sönke","last_name":"Sievers","first_name":"Sönke"},{"full_name":"Klobucnik, Jan","last_name":"Klobucnik","first_name":"Jan"},{"last_name":"Miersch","full_name":"Miersch, David","first_name":"David"}],"date_created":"2021-01-05T11:44:45Z","date_updated":"2022-01-06T06:54:41Z","main_file_link":[{"url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2237757"}],"doi":"10.2139/ssrn.2237757","title":"Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence","publication_status":"published","jel":["C63","C52","C53","G33","M41"],"citation":{"ama":"Sievers S, Klobucnik J, Miersch D. <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>.; 2017. doi:<a href=\"https://doi.org/10.2139/ssrn.2237757\">10.2139/ssrn.2237757</a>","ieee":"S. Sievers, J. Klobucnik, and D. Miersch, <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. 2017.","chicago":"Sievers, Sönke, Jan Klobucnik, and David Miersch. <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>, 2017. <a href=\"https://doi.org/10.2139/ssrn.2237757\">https://doi.org/10.2139/ssrn.2237757</a>.","apa":"Sievers, S., Klobucnik, J., &#38; Miersch, D. (2017). <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. <a href=\"https://doi.org/10.2139/ssrn.2237757\">https://doi.org/10.2139/ssrn.2237757</a>","bibtex":"@book{Sievers_Klobucnik_Miersch_2017, title={Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}, DOI={<a href=\"https://doi.org/10.2139/ssrn.2237757\">10.2139/ssrn.2237757</a>}, author={Sievers, Sönke and Klobucnik, Jan and Miersch, David}, year={2017} }","short":"S. Sievers, J. Klobucnik, D. Miersch, Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence, 2017.","mla":"Sievers, Sönke, et al. <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. 2017, doi:<a href=\"https://doi.org/10.2139/ssrn.2237757\">10.2139/ssrn.2237757</a>."},"page":"84","year":"2017","user_id":"46447","department":[{"_id":"275"}],"_id":"20868","language":[{"iso":"eng"}],"keyword":["Financial distress prediction","probability of default","accounting information","stochastic processes","simulation"],"type":"working_paper","status":"public","abstract":[{"text":"This study proposes a simple theoretical framework that allows for assessing financial distress up to five years in advance. We jointly model financial distress by using two of its key driving factors: declining cash-generating ability and insufficient liquidity reserves. The model is based on stochastic processes and incorporates firm-level and industry-sector developments. A large-scale empirical implementation for US-listed firms over the period of 1980-2010 shows important improvements in the discriminatory accuracy and demonstrates incremental information content beyond state-of-the-art accounting and market-based prediction models. Consequently, this study might provide important ex ante warning signals for investors, regulators and practitioners.","lang":"eng"}]},{"citation":{"ama":"Klobucnik J, Miersch D, Sievers S. Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. <i>SSRN Electronic Journal</i>. 2017.","ieee":"J. Klobucnik, D. Miersch, and S. Sievers, “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence,” <i>SSRN Electronic Journal</i>, 2017.","chicago":"Klobucnik, Jan, David Miersch, and Sönke Sievers. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” <i>SSRN Electronic Journal</i>, 2017.","apa":"Klobucnik, J., Miersch, D., &#38; Sievers, S. (2017). Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. <i>SSRN Electronic Journal</i>.","mla":"Klobucnik, Jan, et al. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” <i>SSRN Electronic Journal</i>, 2017.","bibtex":"@article{Klobucnik_Miersch_Sievers_2017, title={Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}, journal={SSRN Electronic Journal}, author={Klobucnik, Jan and Miersch, David and Sievers, Sönke}, year={2017} }","short":"J. Klobucnik, D. Miersch, S. Sievers, SSRN Electronic Journal (2017)."},"jel":["C63","C52","C53","G33","M41"],"year":"2017","publication_status":"published","title":"Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence","date_created":"2018-10-31T12:19:42Z","author":[{"last_name":"Klobucnik","full_name":"Klobucnik, Jan","first_name":"Jan"},{"first_name":"David","full_name":"Miersch, David","last_name":"Miersch"},{"last_name":"Sievers","full_name":"Sievers, Sönke","first_name":"Sönke"}],"date_updated":"2022-01-06T07:01:43Z","status":"public","abstract":[{"lang":"eng","text":"This study proposes a simple theoretical framework that allows for assessing financial distress up to five years in advance. We jointly model financial distress by using two of its key driving factors: declining cash-generating ability and insufficient liquidity reserves. The model is based on stochastic processes and incorporates firm-level and industry-sector developments. A large-scale empirical implementation for US-listed firms over the period of 1980-2010 shows important improvements in the discriminatory accuracy and demonstrates incremental information content beyond state-of-the-art accounting and market-based prediction models. Consequently, this study might provide important ex ante warning signals for investors, regulators and practitioners. "}],"type":"journal_article","publication":"SSRN Electronic Journal","language":[{"iso":"eng"}],"keyword":["Financial distress prediction","probability of default","accounting information","stochastic processes","simulation"],"user_id":"64756","department":[{"_id":"275"}],"_id":"5199"},{"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"],"publication":"IEEE Transactions on Audio, Speech, and Language Processing","abstract":[{"lang":"eng","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."}],"date_created":"2019-07-12T05:29:23Z","title":"Model-Based Feature Enhancement for Reverberant Speech Recognition","issue":"7","year":"2010","department":[{"_id":"54"}],"user_id":"44006","_id":"11846","type":"journal_article","status":"public","volume":18,"author":[{"first_name":"Alexander","full_name":"Krueger, Alexander","last_name":"Krueger"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"oa":"1","date_updated":"2022-01-06T06:51:11Z","doi":"10.1109/TASL.2010.2049684","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2010/KrHa10.pdf"}],"page":"1692-1707","intvolume":"        18","citation":{"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>","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} }","short":"A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 18 (2010) 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>.","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>"}},{"date_created":"2019-07-12T05:31:00Z","author":[{"first_name":"Ernst","full_name":"Warsitz, Ernst","last_name":"Warsitz"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"volume":4,"date_updated":"2022-01-06T06:51:12Z","oa":"1","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2005/WaHa05.pdf"}],"doi":"10.1109/ICASSP.2005.1416129","title":"Acoustic filter-and-sum beamforming by adaptive principal component analysis","citation":{"ieee":"E. Warsitz and R. Haeb-Umbach, “Acoustic filter-and-sum beamforming by adaptive principal component analysis,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>, 2005, vol. 4, p. iv/797-iv/800 Vol. 4.","chicago":"Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming by Adaptive Principal Component Analysis.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>, 4:iv/797-iv/800 Vol. 4, 2005. <a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">https://doi.org/10.1109/ICASSP.2005.1416129</a>.","ama":"Warsitz E, Haeb-Umbach R. Acoustic filter-and-sum beamforming by adaptive principal component analysis. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>. Vol 4. ; 2005:iv/797-iv/800 Vol. 4. doi:<a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">10.1109/ICASSP.2005.1416129</a>","bibtex":"@inproceedings{Warsitz_Haeb-Umbach_2005, title={Acoustic filter-and-sum beamforming by adaptive principal component analysis}, volume={4}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">10.1109/ICASSP.2005.1416129</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)}, author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2005}, pages={iv/797-iv/800 Vol. 4} }","short":"E. Warsitz, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005), 2005, p. iv/797-iv/800 Vol. 4.","mla":"Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming by Adaptive Principal Component Analysis.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>, vol. 4, 2005, p. iv/797-iv/800 Vol. 4, doi:<a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">10.1109/ICASSP.2005.1416129</a>.","apa":"Warsitz, E., &#38; Haeb-Umbach, R. (2005). Acoustic filter-and-sum beamforming by adaptive principal component analysis. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)</i> (Vol. 4, p. iv/797-iv/800 Vol. 4). <a href=\"https://doi.org/10.1109/ICASSP.2005.1416129\">https://doi.org/10.1109/ICASSP.2005.1416129</a>"},"intvolume":"         4","page":"iv/797-iv/800 Vol. 4","year":"2005","user_id":"44006","department":[{"_id":"54"}],"_id":"11930","language":[{"iso":"eng"}],"keyword":["acoustic filter-and-sum beamforming","acoustic room impulses","acoustic signal processing","adaptive principal component analysis","adaptive signal processing","architectural acoustics","constrained optimization problem","cross power spectral density","deterministic algorithm","deterministic algorithms","distant-talking environments","eigenvalues and eigenfunctions","eigenvector","enhanced signal","filter-and-sum beamformer","FIR filter coefficients","FIR filter coefficients","FIR filters","gradient methods","human-machine interfaces","iterative estimation","iterative methods","largest eigenvalue","microphone signals","multichannel signal processing","optimisation","principal component analysis","spectral analysis","stochastic gradient ascent algorithm","stochastic processes"],"type":"conference","publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)","status":"public","abstract":[{"text":"For human-machine interfaces in distant-talking environments multichannel signal processing is often employed to obtain an enhanced signal for subsequent processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum beamformer to adjust the coefficients of FIR filters to changing acoustic room impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient ascent algorithm are derived from a constrained optimization problem, which iteratively estimates the eigenvector corresponding to the largest eigenvalue of the cross power spectral density of the microphone signals. The method does not require an explicit estimation of the speaker location. The experimental results show fast adaptation and excellent robustness of the proposed algorithm.","lang":"eng"}]}]