Paul M. Baggenstoss

67 papers B 2C 2Misc 4Journal 35Unranked 24

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	On Maximum Entropy Linear Feature Inversion. CoRR Paul M. Baggenstoss
2024	J	`jnl`	Projected Belief Networks With Discriminative Alignment for Acoustic Event Classification: Rivaling State of the Art CNNs. CoRR Paul M. Baggenstoss, Kevin Wilkinghoff, Felix Govaers, Frank Kurth
2024	—	`conf`	Projected Belief Networks with Discriminative Alignment for Classifying Marine Mammals. EUSIPCO Paul M. Baggenstoss
2023	J	`jnl`	A Comparison of PDF Projection with Normalizing Flows and SurVAE. CoRR Paul M. Baggenstoss, Felix Govaers
2023	—	`conf`	Evaluation of Robustness Metrics for Defense of Machine Learning Systems. ICMCIS J. DeMarchi, R. Rijken, J. Melrose, B. Madahar, Giorgio Fumera, Fabio Roli, Emanuele Ledda, Metin Aktas, Frank Kurth, Paul M. Baggenstoss, Björn Pelzer, L. Kanestad
2023	—	`conf`	Improved Auto-Encoding Using Deterministic Projected Belief Networks and Compound Activation Functions. EUSIPCO Paul M. Baggenstoss
2023	J	`jnl`	Improved Auto-Encoding using Deterministic Projected Belief Networks. CoRR Paul M. Baggenstoss
2023	—	`conf`	Novel Generative Classifier for Acoustic Events. EUSIPCO Paul M. Baggenstoss, Kevin Wilkinghoff
2022	J	`jnl`	Nonlinear Dimension Reduction by PDF Estimation. IEEE Trans. Signal Process. Paul M. Baggenstoss, Steven Kay
2022	—	`conf`	Trainable Compound Activation Functions for Machine Learning. EUSIPCO Paul M. Baggenstoss
2022	J	`jnl`	Trainable Compound Activation Functions for Machine Learning. CoRR Paul M. Baggenstoss
2022	—	`conf`	Using the Projected Belief Network at High Dimensions. EUSIPCO Paul M. Baggenstoss, Frank Kurth
2022	J	`jnl`	Using the Projected Belief Network at High Dimensions. CoRR Paul M. Baggenstoss, Frank Kurth
2021	J	`jnl`	Discriminative Alignment of Projected Belief Networks. IEEE Signal Process. Lett. Paul M. Baggenstoss
2021	J	`jnl`	Maximum Entropy Auto-Encoding. CoRR Paul M. Baggenstoss
2021	—	`conf`	New Restricted Boltzmann Machines and Deep Belief Networks for Audio Classification. ITG Conference on Speech Communication Paul M. Baggenstoss
2021	C	`conf`	On a Detection Method of Adversarial Samples for Deep Neural Networks. FUSION Felix Govaers, Paul M. Baggenstoss
2021	—	`conf`	Separation of Bird Calls and DOA estimation using a 4-Microphone Array. EUSIPCO Paul M. Baggenstoss, Karl-Heinz Frommolt, Olaf Jahn, Frank Kurth
2020	Misc	`conf`	A Neural Network Based on First Principles. ICASSP Paul M. Baggenstoss
2020	J	`jnl`	A Neural Network Based on First Principles. CoRR Paul M. Baggenstoss
2020	J	`jnl`	The Projected Belief Network Classfier : both Generative and Discriminative. CoRR Paul M. Baggenstoss
2020	—	`conf`	The Projected Belief Network Classifier: both Generative and Discriminative. EUSIPCO Paul M. Baggenstoss
2019	—	`conf`	Applications of Projected Belief Networks (PBN). EUSIPCO Paul M. Baggenstoss
2019	—	`conf`	Efficient Phase-Based Acoustic Tracking of Drones using a Microphone Array. EUSIPCO Paul M. Baggenstoss, Mark Springer, Marc Oispuu, Frank Kurth
2019	J	`jnl`	On the Duality Between Belief Networks and Feed-Forward Neural Networks. IEEE Trans. Neural Networks Learn. Syst. Paul M. Baggenstoss
2018	—	`conf`	Acoustic Event Classification Using Multi-Resolution HMM. EUSIPCO Paul M. Baggenstoss
2018	J	`jnl`	Beyond Moments: Extending the Maximum Entropy Principle to Feature Distribution Constraints. Entropy Paul M. Baggenstoss
2018	—	`conf`	Robust Speaker Identification by Fusing Classification Scores with a Neural Network. ITG Symposium on Speech Communication Kevin Wilkinghoff, Paul M. Baggenstoss, Alessia Cornaggia-Urrigshardt, Frank Kurth
2017	—	`conf`	Evaluating the RBM without integration using PDF projection. EUSIPCO Paul M. Baggenstoss
2017	—	`conf`	Glottal mixture model (GLOMM) for speaker identification on telephone channels. EUSIPCO Paul M. Baggenstoss, Kevin Wilkinghoff, Frank Kurth
2017	—	`conf`	Kernel-based generative learning in distortion feature space. SSCI Bo Tang, Paul M. Baggenstoss, Haibo He
2017	J	`jnl`	Uniform Manifold Sampling (UMS): Sampling the Maximum Entropy PDF. IEEE Trans. Signal Process. Paul M. Baggenstoss
2016	J	`jnl`	A Bayesian Classification Approach Using Class-Specific Features for Text Categorization. IEEE Trans. Knowl. Data Eng. Bo Tang, Haibo He, Paul M. Baggenstoss, Steven Kay
2016	J	`jnl`	Class-specific model mixtures for the classification of acoustic time series. IEEE Trans. Aerosp. Electron. Syst. Paul M. Baggenstoss, Brian F. Harrison
2016	—	`conf`	Combining the glottal mixture model (GLOMM) with UBM for speaker recognition. EUSIPCO Paul M. Baggenstoss
2016	J	`jnl`	EEF: Exponentially Embedded Families With Class-Specific Features for Classification. IEEE Signal Process. Lett. Bo Tang, Steven Kay, Haibo He, Paul M. Baggenstoss
2016	J	`jnl`	EEF: Exponentially Embedded Families with Class-Specific Features for Classification. CoRR Bo Tang, Steven Kay, Haibo He, Paul M. Baggenstoss
2016	J	`jnl`	Kernel-based Generative Learning in Distortion Feature Space. CoRR Bo Tang, Paul M. Baggenstoss, Haibo He
2016	C	`conf`	Maximum entropy feature fusion. FUSION Paul M. Baggenstoss
2015	J	`jnl`	Car detection by fusion of HOG and causal MRF. IEEE Trans. Aerosp. Electron. Syst. Satish Madhogaria, Paul M. Baggenstoss, Marek Schikora, Wolfgang Koch, Daniel Cremers
2015	—	`conf`	Class-specific model mixtures for the classification of time-series. EUSIPCO Paul M. Baggenstoss
2015	—	`conf`	Derivative-augmented features as a dynamic model for time-series. EUSIPCO Paul M. Baggenstoss
2015	J	`jnl`	Maximum Entropy PDF Design Using Feature Density Constraints: Applications in Signal Processing. IEEE Trans. Signal Process. Paul M. Baggenstoss
2014	B	`conf`	Optimal Detection and Classification of Diverse Short-duration Signals. IC2E Paul M. Baggenstoss
2014	J	`jnl`	Recursive Decimation/Interpolation for ML Chirp Parameter Estimation. IEEE Trans. Aerosp. Electron. Syst. Paul M. Baggenstoss
2013	J	`jnl`	Specular Decomposition of Active Sonar Returns using Combined Waveforms. IEEE Trans. Aerosp. Electron. Syst. Paul M. Baggenstoss
2012	J	`jnl`	On the Equivalence of Hanning-Weighted and Overlapped Analysis Windows Using Different Window Sizes. IEEE Signal Process. Lett. Paul M. Baggenstoss
2011	J	`jnl`	Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields. IEEE Trans. Aerosp. Electron. Syst. Paul M. Baggenstoss
2010	J	`jnl`	A multi-resolution hidden Markov model using class-specific features. IEEE Trans. Signal Process. Paul M. Baggenstoss
2010	—	`conf`	Class-specific classifiers in audio-visual speech recognition. EUSIPCO Virginia Estellers, Paul M. Baggenstoss, Jean-Philippe Thiran
2008	—	`conf`	A multi-resolution hidden Markov model using class-specific features. EUSIPCO Paul M. Baggenstoss
2008	—	`conf`	Iterated class-specific subspaces for speaker-dependent phoneme classification. EUSIPCO Paul M. Baggenstoss
2004	J	`jnl`	Image Distortion Analysis Using Polynomial Series Expansion. IEEE Trans. Pattern Anal. Mach. Intell. Paul M. Baggenstoss
2004	—	`conf`	Speech Music Discrimination Using Class-Specific Features. ICPR (2) Thomas Beierholm, Paul M. Baggenstoss
2003	Misc	`conf`	A New Optimal Classifier Architecture to Aviod the Dimensionality Curse. IbPRIA Paul M. Baggenstoss
2003	J	`jnl`	The PDF projection theorem and the class-specific method. IEEE Trans. Signal Process. Paul M. Baggenstoss
2002	—	`conf`	The Chain-Rule Processor: Optimal Classification Through Signal Processing. ICPR (1) Paul M. Baggenstoss
2001	J	`jnl`	A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. IEEE Trans. Speech Audio Process. Paul M. Baggenstoss
2001	J	`jnl`	Multidimensional probability density function approximations for detection, classification, and model order selection. IEEE Trans. Signal Process. Steven M. Kay, Albert H. Nuttall, Paul M. Baggenstoss
2000	B	`conf`	A Theoretically Optimal Probabilistic Classifier Using Class-Specific Features. ICPR Paul M. Baggenstoss, Heinrich Niemann
2000	Misc	`conf`	A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. ICASSP Paul M. Baggenstoss
1999	Misc	`conf`	An E-M algorithm for joint model estimation. ICASSP Paul M. Baggenstoss, Tod Luginbuhl
1999	J	`jnl`	Class-specific feature sets in classification. IEEE Trans. Signal Process. Paul M. Baggenstoss
1995	J	`jnl`	Detection of broadband planewaves in the presence of Gaussian noise of unknown covariance: asymptotically optimum tests using the 2-D autoregressive noise model. IEEE Trans. Signal Process. Paul M. Baggenstoss, Steven M. Kay
1993	J	`jnl`	On the estimation of rational transfer functions from samples of the power spectrum. IEEE Trans. Signal Process. Paul M. Baggenstoss, Ramdas Kumaresan
1992	J	`jnl`	An adaptive detector for deterministic signals in noise of unknown spectra using the Rao test. IEEE Trans. Signal Process. Paul M. Baggenstoss, Steven M. Kay
1991	J	`jnl`	On estimating the angle parameters of an exponential signal at high SNR. IEEE Trans. Signal Process. Paul M. Baggenstoss, Steven M. Kay