Paul M. Baggenstoss

Publication Activity (10 Years)

Years Active: 1991-2024
Publications (10 Years): 42

Top Topics

Belief Networks

Machine Learning

Specific Features

Maximum Entropy

Top Venues

IEEE Trans. Signal Process.

IEEE Trans. Aerosp. Electron. Syst.

Publications

Paul M. Baggenstoss
On Maximum Entropy Linear Feature Inversion. CoRR (2024)
Paul M. Baggenstoss, Kevin Wilkinghoff, Felix Govaers, Frank Kurth
Projected Belief Networks With Discriminative Alignment for Acoustic Event Classification: Rivaling State of the Art CNNs. CoRR (2024)
Paul M. Baggenstoss, Kevin Wilkinghoff
Novel Generative Classifier for Acoustic Events. EUSIPCO (2023)
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
Evaluation of Robustness Metrics for Defense of Machine Learning Systems. ICMCIS (2023)
Paul M. Baggenstoss, Felix Govaers
A Comparison of PDF Projection with Normalizing Flows and SurVAE. CoRR (2023)
Paul M. Baggenstoss
Improved Auto-Encoding Using Deterministic Projected Belief Networks and Compound Activation Functions. EUSIPCO (2023)
Paul M. Baggenstoss
Improved Auto-Encoding using Deterministic Projected Belief Networks. CoRR (2023)
Paul M. Baggenstoss, Steven Kay
Nonlinear Dimension Reduction by PDF Estimation. IEEE Trans. Signal Process. 70 (2022)
Paul M. Baggenstoss
Trainable Compound Activation Functions for Machine Learning. EUSIPCO (2022)
Paul M. Baggenstoss
Trainable Compound Activation Functions for Machine Learning. CoRR (2022)
Paul M. Baggenstoss
Using the Projected Belief Network at High Dimensions. CoRR (2022)
Paul M. Baggenstoss, Frank Kurth
Using the Projected Belief Network at High Dimensions. EUSIPCO (2022)
Paul M. Baggenstoss
Maximum Entropy Auto-Encoding. CoRR (2021)
Paul M. Baggenstoss
Discriminative Alignment of Projected Belief Networks. IEEE Signal Process. Lett. 28 (2021)
Paul M. Baggenstoss
New Restricted Boltzmann Machines and Deep Belief Networks for Audio Classification. ITG Conference on Speech Communication (2021)
Felix Govaers, Paul M. Baggenstoss
On a Detection Method of Adversarial Samples for Deep Neural Networks. FUSION (2021)
Paul M. Baggenstoss, Karl-Heinz Frommolt, Olaf Jahn, Frank Kurth
Separation of Bird Calls and DOA estimation using a 4-Microphone Array. EUSIPCO (2021)
Paul M. Baggenstoss
A Neural Network Based on First Principles. CoRR (2020)
Paul M. Baggenstoss
A Neural Network Based on First Principles. ICASSP (2020)
Paul M. Baggenstoss
The Projected Belief Network Classifier: both Generative and Discriminative. EUSIPCO (2020)
Paul M. Baggenstoss
The Projected Belief Network Classfier : both Generative and Discriminative. CoRR (2020)
Paul M. Baggenstoss
Applications of Projected Belief Networks (PBN). EUSIPCO (2019)
Paul M. Baggenstoss, Mark Springer, Marc Oispuu, Frank Kurth
Efficient Phase-Based Acoustic Tracking of Drones using a Microphone Array. EUSIPCO (2019)
Paul M. Baggenstoss
On the Duality Between Belief Networks and Feed-Forward Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 30 (1) (2019)
Kevin Wilkinghoff, Paul M. Baggenstoss, Alessia Cornaggia-Urrigshardt, Frank Kurth
Robust Speaker Identification by Fusing Classification Scores with a Neural Network. ITG Symposium on Speech Communication (2018)
Paul M. Baggenstoss
Beyond Moments: Extending the Maximum Entropy Principle to Feature Distribution Constraints. Entropy 20 (9) (2018)
Paul M. Baggenstoss
Acoustic Event Classification Using Multi-Resolution HMM. EUSIPCO (2018)
Bo Tang, Paul M. Baggenstoss, Haibo He
Kernel-based generative learning in distortion feature space. SSCI (2017)
Paul M. Baggenstoss
Evaluating the RBM without integration using PDF projection. EUSIPCO (2017)
Paul M. Baggenstoss
Uniform Manifold Sampling (UMS): Sampling the Maximum Entropy PDF. IEEE Trans. Signal Process. 65 (9) (2017)
Paul M. Baggenstoss, Kevin Wilkinghoff, Frank Kurth
Glottal mixture model (GLOMM) for speaker identification on telephone channels. EUSIPCO (2017)
Bo Tang, Paul M. Baggenstoss, Haibo He
Kernel-based Generative Learning in Distortion Feature Space. CoRR (2016)
Paul M. Baggenstoss
Combining the glottal mixture model (GLOMM) with UBM for speaker recognition. EUSIPCO (2016)
Paul M. Baggenstoss, Brian F. Harrison
Class-specific model mixtures for the classification of acoustic time series. IEEE Trans. Aerosp. Electron. Syst. 52 (4) (2016)
Paul M. Baggenstoss
Maximum entropy feature fusion. FUSION (2016)
Bo Tang, Haibo He, Paul M. Baggenstoss, Steven Kay
A Bayesian Classification Approach Using Class-Specific Features for Text Categorization. IEEE Trans. Knowl. Data Eng. 28 (6) (2016)
Bo Tang, Steven Kay, Haibo He, Paul M. Baggenstoss
EEF: Exponentially Embedded Families with Class-Specific Features for Classification. CoRR (2016)
Bo Tang, Steven Kay, Haibo He, Paul M. Baggenstoss
EEF: Exponentially Embedded Families With Class-Specific Features for Classification. IEEE Signal Process. Lett. 23 (7) (2016)
Paul M. Baggenstoss
Class-specific model mixtures for the classification of time-series. EUSIPCO (2015)
Paul M. Baggenstoss
Maximum Entropy PDF Design Using Feature Density Constraints: Applications in Signal Processing. IEEE Trans. Signal Process. 63 (11) (2015)
Satish Madhogaria, Paul M. Baggenstoss, Marek Schikora, Wolfgang Koch, Daniel Cremers
Car detection by fusion of HOG and causal MRF. IEEE Trans. Aerosp. Electron. Syst. 51 (1) (2015)
Paul M. Baggenstoss
Derivative-augmented features as a dynamic model for time-series. EUSIPCO (2015)
Paul M. Baggenstoss
Recursive Decimation/Interpolation for ML Chirp Parameter Estimation. IEEE Trans. Aerosp. Electron. Syst. 50 (1) (2014)
Paul M. Baggenstoss
Optimal Detection and Classification of Diverse Short-duration Signals. IC2E (2014)
Paul M. Baggenstoss
Specular Decomposition of Active Sonar Returns using Combined Waveforms. IEEE Trans. Aerosp. Electron. Syst. 49 (4) (2013)
Paul M. Baggenstoss
On the Equivalence of Hanning-Weighted and Overlapped Analysis Windows Using Different Window Sizes. IEEE Signal Process. Lett. 19 (1) (2012)
Paul M. Baggenstoss
Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields. IEEE Trans. Aerosp. Electron. Syst. 47 (2) (2011)
Paul M. Baggenstoss
A multi-resolution hidden Markov model using class-specific features. IEEE Trans. Signal Process. 58 (10) (2010)
Virginia Estellers, Paul M. Baggenstoss, Jean-Philippe Thiran
Class-specific classifiers in audio-visual speech recognition. EUSIPCO (2010)
Paul M. Baggenstoss
Iterated class-specific subspaces for speaker-dependent phoneme classification. EUSIPCO (2008)
Paul M. Baggenstoss
A multi-resolution hidden Markov model using class-specific features. EUSIPCO (2008)
Thomas Beierholm, Paul M. Baggenstoss
Speech Music Discrimination Using Class-Specific Features. ICPR (2) (2004)
Paul M. Baggenstoss
Image Distortion Analysis Using Polynomial Series Expansion. IEEE Trans. Pattern Anal. Mach. Intell. 26 (11) (2004)
Paul M. Baggenstoss
A New Optimal Classifier Architecture to Aviod the Dimensionality Curse. IbPRIA (2003)
Paul M. Baggenstoss
The PDF projection theorem and the class-specific method. IEEE Trans. Signal Process. 51 (3) (2003)
Paul M. Baggenstoss
The Chain-Rule Processor: Optimal Classification Through Signal Processing. ICPR (1) (2002)
Paul M. Baggenstoss
A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. IEEE Trans. Speech Audio Process. 9 (4) (2001)
Steven M. Kay, Albert H. Nuttall, Paul M. Baggenstoss
Multidimensional probability density function approximations for detection, classification, and model order selection. IEEE Trans. Signal Process. 49 (10) (2001)
Paul M. Baggenstoss, Heinrich Niemann
A Theoretically Optimal Probabilistic Classifier Using Class-Specific Features. ICPR (2000)
Paul M. Baggenstoss
A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. ICASSP (2000)
Paul M. Baggenstoss, Tod Luginbuhl
An E-M algorithm for joint model estimation. ICASSP (1999)
Paul M. Baggenstoss
Class-specific feature sets in classification. IEEE Trans. Signal Process. 47 (12) (1999)
Paul M. Baggenstoss, Steven M. Kay
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. 43 (4) (1995)
Paul M. Baggenstoss, Ramdas Kumaresan
On the estimation of rational transfer functions from samples of the power spectrum. IEEE Trans. Signal Process. 41 (3) (1993)
Paul M. Baggenstoss, Steven M. Kay
An adaptive detector for deterministic signals in noise of unknown spectra using the Rao test. IEEE Trans. Signal Process. 40 (6) (1992)
Paul M. Baggenstoss, Steven M. Kay
On estimating the angle parameters of an exponential signal at high SNR. IEEE Trans. Signal Process. 39 (5) (1991)